{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.imshow(train_images[1])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "train_images = train_images / 255.0\n",
    "test_images = test_images / 255.0\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_3 (Flatten)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 101,770\n",
      "Trainable params: 101,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(128, activation=tf.nn.relu),\n",
    "    keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam', \n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 6s 100us/sample - loss: 3.0744 - accuracy: 0.6726\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.7389 - accuracy: 0.7142\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 5s 83us/sample - loss: 0.6300 - accuracy: 0.7473\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.5631 - accuracy: 0.7987\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 5s 86us/sample - loss: 0.5366 - accuracy: 0.8128\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x13bce6c18>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 63us/sample - loss: 0.6564 - accuracy: 0.7953\n",
      "Test accuracy: 0.7953\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "\n",
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  \n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "  \n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array[i], true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1]) \n",
    "  predicted_label = np.argmax(predictions_array)\n",
    " \n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 6\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 2\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a+XIwkyZNCtbNnDnTxxMmTMjdhh3GtCkSXbt2DdrZ5U6dOgXrbBqDTZdYt25d0G7u3Lk+tsOldtY6KRyqraqq8vExxxwTtKuoqPDx3/72Nx/HZWuKDSPZoeC+ffv6uGPHjkG76rQQZr5DbYvTEfI+Y3FZNztrY9u2bYN1pc6mZ8UpTXlsqkex4dvHH3/cx7avStL06dN9HPfPDRs2lLQfQEN36qmnBss2Xc/2o2KzRBZjj1PFZrHcn+8DYH/U+4kx0JhQmxUAgPJFKgUAAACgBnDFuFmzZn72t1tuuSW3nZ1JavLkycE6m97w2muv+XjRokVBuxkzZvjYVnKQwiEcOxwbD5HadIwRI0b4+JxzzgnajR071sd26KmYSy65xMfvvvtusK5bt24+jlMkbPqIHdKOZwoaOnToPu0PUKq4n7z//vsF29kqFFKYMhR/Xm3ahR2yLTbcmje7nZSfZlFsiNZ+h8RpUA899FDutuOUEaBc9e/fP1i2xx/bf+M+v2DBAh8PHDgwd/s2FbJYvyEFEAcLV4wBAAAAcWIMAAAASOLEGAAAAJDUAHKMS2VncTv77LODdXb5+uuvP2j7VNueeOKJg/I6zCCE2hbnB+fl7cZlzGxeYryNUmfPs8t5s9vFy8VykW0px4kTJ/q4Oke/kPi17KyYQGNi84ptqUNb8lQqPcfYzmprc/q7dOkStCPHGAcLZ0gAAACAODEGAAAAJJVRKgWAhsuWXJLCWexsqcWvfvWrQbvnn3/ex3H6QakpP3npE6XOohW/zqZNm3x8xhln+Piiiy4K2v3Hf/yHj+O0j3jmSqCc5JUvlaTLLrvMx/fff7+P4/QpO1ttXM7Uime8LLQP0odTK4C6whVjAAAAQJwYAwAAAJI4MQYAAAAkkWMMoBbEU6zbnNtiU7726NHDx++8806wzpZ4KjZtc6ny8ibj/GhbUq5nz54+7t69e+624zzlxYsX7/d+AvWtWI7xpZde6uPf/e53Pm7ZsmXQ7uGHH/bxt771rdzXsmXYipVbjMs5AnWFK8YAAACAODEGAAAAJJFKAaAWnHbaacGynTGudevWPo5nj3v77bfrdsdqkZ3JS5I6dOjg47g824knnnhQ9gmoC8XKGV544YU+tiXU4j5QarnFo48+2sczZ870sf3ekKQVK1aUtD3gQHHFGAAAABAnxgAAAIAkUikA1II4dcDOYmfvVi91eLUhiitq2KHjnTt3BuvatWt3UPYJqAvxTI55+vfv7+NJkyYF69577z0fv/baaz4+9dRTg3a2KsX777/v47hPrV27tqR9Ag5U+R6lAAAAgFrEiTEAAAAgUikAAGXspptuyl13++23H8Q9AdAYcGIM4ID169cvWB41apSPbdmlYrm3u3fvDpZtnqOdiauu2dey+zB48OCg3cc+9jEfb9y4MVh3yimn1NHeAXUvnnUuz7XXXuvjYcOGBeuuuOIKH8d5xdZVV13l402bNvm4ffv2QbuPfvSjJe0TcKBIpQAAAADEiTEAAAAgSXIHc4iy4A44t0bS4nrdiaanf5IkPWprY/wO6x2/z8aB32Pjwu+zceD32HiU9Lus9xNjAAAAoCEglQIAAAAQJ8YAAACAJE6MAQAAAEklnhg7p487p8Q5Ddt7a8k5LXJO3Qs8vnVfdm5f2xfZzmecU9+cdX9yTpXZv0XOqTJ7/FznNM05zcz+Pyt7vJVz+qtzmuWcrjfbuds5HVdkHz7unG6JHqt0Tn/ch/fwswKPf8s5fa2UbexP+yLbqXBOnzLLI5zTbw90u6g7zmlP9pmb5ZwedE5t99L+t87p8iz+u3MafXD21H9Ol5m+OTZ7vKVz+k3WL6uc0xnZ4wfcL6PXnOWcLtnLPlY4p1lZfIZzeqoW3nqx16OPNRLOqZv5bK+MPust63v/qjmn253TUue0MXq8tXN6yDnNc04TndPhZt3N2eNznNM52WO9nNOrWb+62LR90jn1LvL6X3NOn3JOv8h+Nm85p+3mZ3VZXbzvfeGc7nRO+YWaUXZKvWJ8paQJ2f/l6DNS4RPjJNG4JNGxSaJjJT0s6ZFs1VpJFyeJRki6RtJ92ePnK/1ZHCPpKklyTiMlNUsSTS+yD/9P0l3VC87pSEnNJH3UOeXPetBwVUg1J8ZJopmSDrVfkGhwtmef9aMl7ZT0T/W9Q9WcU7MCD/+oum8miZ7OHrtWkrJ+ea6kHzinQ1RL/bL6NSX9H0n3Ztuud86pOX2s8UgSrTPHnV8o/KzvlCTn5A7m58+5ghN+PS7p5AKPf0HSyiTRYEn/I+m2bBvHSPqEpOGSPibp59l7+L+S7pR0kqSvZG0vkzQ5SbQyZ39aSLpa0p+SRP+U/awukTTX/KweLeE91Jnse+tnkv6/g/m6qFt77XTOqb2kj0j6R0lXmMfPyK4iPZT9ZfgH5+Si57ZxTs84p2ujzco5/btzet05zXBO/1Hk9X/knN50Ti84px7ZY8c6p0nZcx91Tl3yHs+ueI2W9IfsL8w2Oa/jJH1S0gOSlCR6I0m0PFv9pqQ2zqmVpF2S2kpqIfn3+1+SvlnkPQyVtCNJtNY8fKXSk+3nJF1q2v7dOX3POU1xTm87pw9N9+OcPpb9ld49enxQdtVsmnN6xeVf4R+ZPf+d6t9N9iX8/ewv+pnOaVyxxyXdrvSkvtK59ItO0pMynxE0aK9IGmyvekr+Cs23ij3ROV2ZfRZmOafvZY/9k3P6vmnjRzic06ezz3Olc/pl9Umwc9rqnH7gnKoklTpV3HBJ4yUpSbRa0kal/bu2+qWybc+WtFtSd2eunFfvd7EddE5dndNj2ffQJOd0jHM6xKUjUp1Nu3dceiWth3N6OPs+fN05nZat/5Zzus85vaqaP8zpY42Ycxrs0quif1B63OmT9Z/q/vbdrF1zZ67iOqcrnNM9Jp7l0hGVF037H2b9cIZz+nz2+DnZMecpSTPj/UkSTZQKnrheKul3WfxnpX+YVj/+QJJoZ5JovqR3JR2vmv7ZWtIel14V/xdJPyjy4zhX0pQk0Z69/MwmZe9tqqQvZsfBl7L3+ZzLRoud0x+d00XmeVuz/w9z6dXsyuznfFL2+EXZtt9wTg9Unzu49Ar/bc7pDUmXJInmSqpwTl2L7SfKRyl/jV4q6a9JorclrXNOx5t1oyTdoPRgNVBKv9Az7ZV+iT+QJPqV3aBzOk/SEEknSjpW0vHO6fQCr91O0tQk0VGSXpJ0a/b47yXdmCQ6Rmlnzn08SfSQpKmS/m/2F+b2nPf5UUmrkkTvFFj3D5KmJ4l2SPqb0qulkyT91KXDrdPNSXQhp0kfumo1TtIflZ6Ix1fimyeJTlT6s73VrnDpX9k3SRpb4IB+t6R/SRIdL+lrCq+EWcdIOkvpycgt2RfHJ5T+LkZKOkfS951TnyKP3yTplexn+qNsu1OlD5/Io2Fx6VWVC1XgQFjCc/tK+p7Sz8+xkk5wTh9XOtpihzXHSfqjS0dGxkk6Lbvis0fp1SMp7d+Tk0Qjk0QTCrzcl7KD270u++NXUpWkS7ID/QClB93DVHv9svp9niTpA0lrijw/z39IeiP7Hvq6pN8niT5QevXtMrP9xUmiVZJ+ovSK4QlKv2vuMdsaLumcJPHfEfSxxm+Y0s/DcKV/5H1b0plKj7en2ZO7HLdKOjtJNFI1ffILklZnx5UTJP2zqxl5GC3p+iTRkfuwj/0kLZGk7Ar3tuyPPv94Zmn22P9KulzSs5K+I+lLku4tcjyW0v45rcT9cUmi0UmiO5Vegb8r63+PSfrhXp57taRHsu+nYyW96dL0jq9JOjNJNErSHKUn8tVWJIlGmSvWlSr9j3s0cKUMO1yp9ItbSk/krlTNh3VKkmiplObLKj0wVR/gHpd0R5LoDwW2eV72741sub3SE+WXo3YfSPpTFv+vpEecUydJnZNEL2WP/07Sg3mPl/D+7Pt8IH7QOR2l9ETgPElKEu1WlkLg0qGeZyVd6px+KOlwpQfBJ6LN9JE5wLo0V3Ntkuhd57RM6ZBt1yTR+qxJdTrHNKU/02pnKf0SOy9JtDnaz/aSTlX6s6jWKue9Pp59IW3PriicqHRU4IHsr/NVzuklpV+geY9vLrDd1cpJWUGD0Cbrp1J6xfjX2vff1wmS/p4k6ec5u7J1epLoMee0wDmdLOkdpQf3VyX9s9KT19ezz2UbpZ8TKT1JfjjndX6u9Ipvkv3/A0mfk3SvpCOVniAulvSapD210S8zX3FOn5a0RdK4JFHinPbVR5Se4CpJNN6l+aQdlX6X3SLpN0qv+lZ/t50jabh5nY5Zf5akJ6KTB/pY4zc/STQ1i0+SNL76Iohzul/S6ZL+WuT5r0r6vXN6UDXHkvMkHemcH23opPSYK0kTk0Tv1uYbiCWJNkj+PoFuSlMPLs+ucndWeq4wJXpaH9WcI+yNvVdntLLjtdLzgJv38twpku5yaUrjY0miGc7pAqV/lE7M+mVLSX83z/lTtA36ZSNS9MQ4Gxo4S9II55QozYlNnNO/Z012mOZ7ou29KukC53R/kiieRcRJui1J9Mt93N86mY0ku4L2CSm4Gi7ndKikRyVdnQ0Lxa5XepX6ZEmblF4ZGy996AC8XekXUbUrJQ1zTouy5Y5KD6TVV9arf67xz3S+0ivzQyX/xVntEEkbs7969yb+OdbWz7W1VPQKAOrX9vjz4Zx2Kxw5an0A2/+j0nSkOZIezU4qnaTfJUnBHLz384ZJsyup1fv4Kym9sS07Af6KWfeapLejp+9vv5TSK3X/HT3mf0YuzZfc35ujJipNX+kh6eNKrwQq2/bJSaL3bePsgLwt2gZ9rPGLf+eFfCAFqYu2316r9IT6IknTndOorO31SaIX7EZcenNcKa8XW6Z0pGZllhbRLkm0MbvQc5hpd2jW1rpV6R+7n5b0otJ++Welo1jWdpX+fVTKe7D9uEV1nCT6m3M6U2lO9B+c07eV/nz/kiT6bImvR79sRPaWSnG5pPuSRP2TRBVJosMkLVRpQ3m3SNqgNDE/9qykz1VfFXFO/ZxTz5z9q87t+5SkCUmiTZI2uJrc26skvZT3eBZvkdShyL6eI2lO9dXvbJ86S/qLpJuSRK/GT8iGdi9SegBuq7QjJVLBHObZkgZnzztE6cnDiOxnWqE0XaWUGxsXKz2B/n12JdvLriAvdE7/J3sd59Kbjwq51KV3FXeTdIak15VeQRznnJplB+7Tlf4lnfd4oZ/pUKkmXxVlYZWkntlVzVbSXodpp0ga45y6uzRX+ErV9LNHVfNZrr6C84LSK0M9JZ9/239vO5Wl61S7TPLVH9pmV3bknM6VtDtJ9JZ53n71y71YpJo/mi9RmsdczCvK0kVcWjVjbZJoc3aB4FGlQ7uzk0TrsvbPyQzTOlf0j1v6WNMyWdKZWf9srnSk4aUsNWeDcxqSHVNsGtPAJNEkpfn1G5SmMjwr6fpsG3JOR7ic+21K9ITSm9Kl9Hj2nHn8SpdWjxkkqb9MOoRL73vpkaVO1Vb/jL2umvMGex6wSDX9+B+U/WHhnCqUpkb8UukV5lFKR77PztbJObV3rui+0C8bkb2dGF8phXd9Kh36LLU6xb8qHb69wz6YJHpO0v1KhylmSnpIhU9ct0k60aU3B50l6T+zx69Rmus6Q2lO0N4e/63ky70U6oBX6MNpFF9S2ilvcTWlYezJ+y2SvpN9QT2r9I+Fmaq5ScZ6WdKo7OrZRyUti3IfX1Y6lNqnwHMDSaI5Sg+6D2ZfPNb/lfSPLr2Z6U2Zm/oiM5T+pT5J0n9l+/Jo9niV0qtr/y+7Wzjv8RlKb6KocjU3352p9I8JlIkk0S6l/WSK0jzdOXtpv0JpfvmLSj8T05JEj2frNig9mPWvHhbNTlpvlvRc1i//Ju39cy7pDpfeCDND6eeq+jPWU+lVsNmSblRWgcLY335ZzK+U/jFQfZPg3q5OfUvpfRMzlN6keo1Z9yelV8rsUOyXJY12aT71WypeLYQ+1oRkF2u+qXQYv1LSpCTxv/8blX7GX5NqLupI+lF2XJ0p6cUk0SxJv1Sa4lSZHU9/rhJSKbNUpEVK03uWOufTEu5WemPgPKXHyq9n+1ulNK93tqSnlV6l/sBs8juSvpHF9yv97E+W9OMCL/8iYmRzAAAgAElEQVS0pDF728cCvqjs/gSlI8H/lj3+c0ljs348XPKjVedKqnLpzXQXS/qf7HvuWkkPZdt5VTkn6c6ptdI/Pmbsx76iAXJJUifZCYg4p59IejJJ9Hx970tdyK42viTpI9lwN9DglVO/pI+hqXFOT0i6IUm0oL73JY9zulLpVfrv1Pe+oHY0iBqdTcR3peITKpS5w5WmnXDARjkpp35JH0NTc6Ma/k1tiWoKFKAR4IoxAAAAIK4YAwAAAJI4MQYAAAAkcWIMAAAASCpt5rs61b1796SiouKgv+7u3eH9K2vW1EyA1axZMx8fckj+3w62XTE2j7t58/BH3qFDTZU6tx9TbO2PRYsWae3atbX2Yvv7O6yqknbn3EbUvLk0Mq8KMwLTpk1bmyRJj9raXn31yWK2baupkPbBBx8E6+LlPLZdixY1pYjbt29fqPlB1xR+j01JY/19zp0718f2mBUfv+xxr2XLlgUfl6Rdu3b5uNjx1j5vyJAhue1qW0P4PXKsrB2l/i7r/cS4oqJCU6fGk7jVPXsiLEm//GXNJHydO3f2cZs2+TXQO3WqmTQr/lLYs6dmQq+dO3f6uGfPcB6TM844w8f2y6MujR49ula3t7+/w2J/B+zeLdXDx6IsOecW1+b2Sv192hPN+ICWd1Pv/v7xN3HiRB+/9957wTrbv2y/i+3YUTNRZ48eNd+Np59++n7tU22rr98j6kZj/X3aY5a9ONSqVaug3fvv10zkaE8E7eOStGqVn+QyuFAU92W7/PTTT+/bTh+AhvB75FhZO0r9Xdb7iXF9efDBB4Plb3/72z7u0qWLj/v0CeciWLhwoY/79evn46FDhwbtZs+e7ePWrWtmtTznnHOCdvZL4aqr4rkKgIbLnuQWq25T7GR4y5YtPh4/fnywbvr06T5+5plnfHzEEUfkbn/r1q0+XrduXdCuW7duPrYH5+98Jyw/evHFF/v4kksu8fHhhx+e8y6Axmvz5s3B8ptvvulj+wdmbPv2mhmS58+f72N7PJTCP6rbtq2pnGj/4N3bawG1iRxjAAAAQJwYAwAAAJI4MQYAAAAkNeEc4/jmO3tzQLE7Y3v37u1jezNAnM+4adMmH3fs2NHHy5YtC9oNGzastB0GGphiOcZ5ecV33313sGzvcI+rS9i+MW7cOB9XVlYG7exNP7baTJyLbG/sadeunY/j74LFi2vuz/jKV75S8DmSdPvtt/u4b9+GPmstsH/im+Vs37bHwPjmcbts79uJb6qzOcz22BtXbih2IzxQm7hiDAAAAIgTYwAAAEBSE06liFMfbCkYW1qma9euQTtbXsoOzW7cuDFoZ4eW84aKJGnEiBH7sttAg2E/48VKst11110+Xr9+fbBuwIABPraTbkjhkKut/z1mzJig3SOPPOJjm+oUD+3avmf7nS0FJ4WTB9ha5TbFQpJuvvlmH997770CGqOHH344WLbHzkMPPdTHcYqETY2y6U5xypQt62ZToWw6oiQtX77cx9OmTfPx8ccfX/wNAPuIK8YAAACAODEGAAAAJDXhVIr+/fsHy1VVVT6201zaWArvTLdDtfEwkh3S3bBhg4+L3XkPlJNiqRRLliwpGA8cODBoZ2eqi9m+ZmeIHDRoUNDOLr/zzjs+jtOgTjrpJB+//PLLPo4rSti78O300/Fd8StXrvTxfffdF6yzs1iWmnICNET33HNPsGxng7UpTraPSlLz5jWnF/Y7wM5uJ4XHWDsrnn2+JK1evdrHU6ZM8TGpFKhtXDEGAAAAxIkxAAAAIIkTYwAAAEBSE84xjnP9bPkmm9sYz+hlS7kVyx0eOnRowdeN8yPjPCqgXBSbIXLevHk+tjmEthyTJLVv397HO3bsCNbZvH3bLi6NeOGFF/p4woQJPo5zgu1r29jeDyBJ27Zt87Etz7hz586gnS1B9cYbbwTrbI4xecUoZ3Z2SkkaPXq0j22ptV27dgXt7DHR9t+4H9m+aMsj2lgKv29s6TagtnHFGAAAABAnxgAAAICkJpxKEQ8DH3bYYT4ePny4j+Nh0AcffNDHdhavN998M2h3+umn+9iWk+nXr1/Qzg4rxWVsgHJl+4MtwRSnS9hUpfjzb4dibTqGnUlSCstHnXfeeQWfEy8PHjy44D5IYRk2O8xry7jFbPkooNytWLHCx3EpUluizZZQi4+ptpypLddmvw+kMM3CpmPEaVf2eTaNCahtXDEGAAAAxIkxAAAAIKkJp1IceeSRwfILL7xQcF08ZHPUUUf5+MQTT/TxF77whaDd4Ycf7uNDDz3Ux126dAnaxXfOA43B0qVLfdyxY0cfx6kUVq9evYJlO+ucHVZt0aJF0M6mbdjqMrZqjBTOcGfvao+rXNgZvGzFijhdasCAAT7u1q1bsM6mSNkhZaAc2HSiYil+Ng0pPpatXbvWx7aSxaxZs4J2dvZLm1YRp3DkzZAH1DauGAMAAADixBgAAACQxIkxAAAAIKkJ5xjb/EUpnO3O5lfFOcGWzXuMcydtqSmbDxXPdGdLQFGCBuXK5uXGbA5hnM97zDHH+DjOHY5zDKvFZdhsv7Hbj2fYsvmQtiyULU0Vb89uI953K575csaMGT62+ZVAOXj77bd9HPdLe6y04tKmtl/ZGWNHjRoVtLMz6/Xv39/HcW6+PXZyrERd4ooxAAAAIE6MAQAAAElNOJUiHg6yqRV2Bh9b1kkK0yeOPfZYH8fDSNu3b/exHY6Nh4fjYSqgHC1YsCBYtmWXbLrQtm3bgna239iZJKUw3aHYrHN5M+TFfdLO0mXXxdu2r2u/F+x7ksL0qThFauHChT4mlQLlZs6cOT6Oy7XZPmz7W5xq1KNHj4LbPvnkk4PlyspKH9t+Gacn2nWUQERd4ooxAAAAIE6MAQAAAEmcGAMAAACSmnCOcTx9pc0rjnMJLbsuLjtj2dxE+1pxmRlyjNEYLFmyJFi2JQrjUmbW4sWLfVxRURGss3mENjff5vlLUocOHXxs+5PddrwfNic4nl7WvpYt3Rjfl2BfK+7HtgQVUG7mzZvn406dOgXr7D0z9nMf34/zmc98puC2P/e5zwXLv/jFL3xc7LvC5jPHJRuB2sQVYwAAAECcGAMAAACSmnAqRTwUY4eEbFmYuORTXppFnJphSz7ZIdj4dRkSQmMQD6Pa1KSOHTv6OC7BtGXLloLPkcKUCdtP4lQK+zy7/XhY1qZcbNiwwcdxKoUttWj3fc2aNUE7O8Qcv1ZVVZWAcrV582Yfx8c2e0y0xzkbS9INN9xQcNsnnHBC7vbySi9KYRoix03UJa4YAwAAAOLEGAAAAJDUhFMpunfvHiznDefYO3ClDw+7VrPDtJKUJEnB5/Tr1y9oFw8fA+Vo69atwbKtKNGlSxcfx5UiLr300txt2D5pU53idAy7bIdz49no8mbSi9OlbH8dNmyYjx9//PGgne27cVUKm44BlBvbd+L0Qdtf7Oe8d+/eQbuBAweW9Fr2WGyPvV27dg3arVu3ruDrArWNszIAAABAnBgDAAAAkjgxBgAAACQ14RzjPn36BMs2l9jmB9sZ7KQPl6SpFpeQsiXabMmnYjP7AOXK5uxKYYmnONfXGj58uI9feeWVYF1eacQ4L3/jxo0+tvnMcTubB2z3yfb32NChQ30c5zXa58UzWm7atCl3m0BD161bNx/HxzbL3hdwwQUX7Ndr2dxkW4Ytvg9o/fr1PuY4irrEFWMAAABAnBgDAAAAkppwKkXbtm1zl+0QbjxkY4dzLJs6IYUlpOwwqx2iAsqZHWKNU4z27NnjY5tyEJc169u3b8F2MZvSFKdmbNu2zce2f8Vl2OyyLScXs/s+ePDggvsQt4vfvx1itnFeegjQkNjPqZ0lUgr7/bx583z8gx/8IHd79jgapzgNGDDAx0uXLvVxjx49gna2v9l2QG3jijEAAAAgTowBAAAASU04lcLe/SqFqRB22Ce+Izce3qk2ZMiQYNnewZ434xZQztauXevjOA3Cpi3YIdA4lcL2r7iv2ZQJWzUmTkewaVC2r8VVJHr27Olj2//jfbfrbKpHsVkqbRUOKXz/K1eu9LFNzQAaKpv+Fx+zbGqQ7Tu2wkzMfgfE/eioo47y8cKFC30czya7Zs0aH9vqM0Bt44oxAAAAIE6MAQAAAEmcGAMAAACSmnCOcczmN9qSbHHeY15uU5xftWTJEh9v3rzZx3EuIlCu7IxzcT9p3bp1wXaHH3540M7mEdqya5LUq1evgtuPSyjanGCbDxnnGNt2Nn85LrW2ZcsWH9t8Srs/8fZsDqUU5l6uXr3ax+QYoxyMGDHCx5MnTw7W2T5m762xM9jFiuXnjx071sc//elPfRyXR7S5+l27ds3dHnCguGIMAAAAiBNjAEAZ691bcq7wvyIXMQGgIFIpMuvWrfOxHR565plngnbXXXddwecfd9xxwfKUKVN83K9fPx/Hw8BAubIlyeISarbc09y5c308bNiwoJ19XjyjnVVsljm7H/Z147QlOwRstxfPkGdTqWwZRzu8LIUpF3GKld2mTcdA7Vu1av/WId+4ceN8/Jvf/CZYZ/upTRMcP3580O68887zcbFZLe13wmGHHebjOP3CbsP2PaC2ccUYAAAAECfGAAAAgCRSKbyXXnrJx/PmzfNxnEpx3333FXz+0UcfHSzb4dif/exnPh45cmTQ7vjjj9/3nQUaAJt+FKdB2IoQmzZt8nH8+bezWdlhWSlMR7DpEzt27Aja2Znv7H7EQ7F2n2xKUzwbn6028e677/p40KBBQbvXXnut4LalcHg4fl9AQ2f7QNw/bGqQbRcfG20qRbE0qe7du/vYVp5YvHhx7uvaqjdAbeOKMQAAACBOjAEAAABJpFIAAPbBTTfdlLvu9ttvP4h7AgC1r8meGMflY2z5JptjbEu3Sfm5TXEOlc2rtKXb4hnCgHI1ffp0H8c5tnZ5lamZFZc1mzp1qo9trrAU5gjbOJ5lrmXLlj62/StuZ5dtWTcbS2Ffrqqq8nHHjh2DdrYcXPz+7axd9j1efvnlAspJXG7Qftbt8dAe5/aXLak4bdq0YJ29zyDub0BtIpUCAAAAECfGAAAAgKQmnEoRz3a1c+dOH9thmniYNY99vhQO+9i0injWLqBc2Vnh7BCoJC1btszHdpaquFybTVXo3LlzsM6mI1hxGpQt32bTJWwpKSmcZc+mX8Tt7HfDokWLfHzJJZcE7f7xH//Rx5/85CeDdTYtpE+fPh9+E0CZOO2004Ll+++/38ddu3b1se1T+6uiosLHGzZsCNbl9XOgtnHFGAAAABAnxgAAAICkJpxKEbPDQHamKjtcXEw8O5C9s92mT/Tu3Xt/dxFoUD772c/mrrN3si9YsMDH8exxjzzyiI/jihV2G3amujjlYu3atT62KU1xeoetWGHjeIa8nj17+njSpEk+vu6664J2dtY+m6YhMTMXGo8vfelLwfJDDz3kY9t3Nm7cGLSz/X7gwIElvVaHDh18bFOwpPA7IP6uAGoTV4wBAAAAcWIMAAAASOLEGAAAAJBEjrFnZ7GyuYml5grGpWpsSSmbG1UbJW2Ahs7m3B5zzDE+jvMG161b52Nb+knKz82Py7jZbdh+F/c1mw9pSz8V65P2tSorK4N1Y8eOzX0e0Fj069cvWLY5/vY+gLhkqZ0Jr9Qc47xZLKWwz8avBdQmrhgDAAAA4sQYAAAAkEQqhbdy5Uof21l1bBpEMXG5Jjtsa7dnUzaAxiKejc72Gzuz3IQJE4J2tqxhzM4eZ7c3b968oF3eMK3t0/E2bLpUXJLR9lE7jPzyyy8H7WwqRfz+45k1gXJiP8/xZ/ncc8/18cMPP+zjOCXp8ccf9/EVV1xR0uva4+jy5ctz96nU4zKwP7hiDAAAAIgTYwAAAEASJ8YAAACAJHKMvV69evl49erVPrb5kcXEU1TmlYay080CjUWch5jXb+bOnRss29JPtp9IYf6xfd6AAQOCdjZHeNmyZbnbs3mJ27dv93GcH2xzJW0c5yxb8fsvlqMJNHR59whIYW69nR46vn9m6dKl+/y6nTp18nFcks0eY9evX7/P2wZKxRVjAAAAQFwxBgDUg5tuuil33e23334Q9wQAanBinLnwwgt9PHXqVB+XmkrRoUOHYNkOCdnSUP3799/fXQTKhi1RaPvQ4sWLg3Y23WHo0KHBOvu8YcOG+TieIe+tt97ysU1bsDPnSWFqhu2vtq9K4RCu3b94xj27rlWrVsE6UilQzmwqYOwjH/mIj205w40bNwbtbOpRVVWVj0eOHJm77Y4dO/o47m8tWrTwsU3BAmobqRQAAACAODEGAAAAJJFK4bVu3drHNvWh1FSKmL3r3Q4JHXroofu1PaCc5KUPfPe73w2Wv//97/v4mWeeCdbZoVlbiSKeLc/2NVv1ZcOGDUG7zZs3F1wXV5uww7Tdu3f38Ze+9KWgXZw+YRUbiq4v5PSiVKWm/xx++OE+rqysDNbZ1Ie//e1vPi6WSrFlyxYf234dW7VqVUn7B+yPhvftDQAAANQDTowBAAAAkUoBAKhlpG0AKFecGGeuvvpqH0+YMMHHtozbvrjkkksKPj5ixIj92h5QTvJybOPZsW655Zbcbbz77rs+tiXZ4vxCmztsZ+yK2ZxHG9s8SUk67bTTfNy+ffvc7QFN3Te+8Q0f9+7dO1hn+9iYMWNK2t64ceN8bGejlcLc/7PPPnuf9hPYF6RSAAAAAOLEGAAAAJAkOTtDU73sgHNrJC3ea0PUpv5JkvSorY3xO6x3/D4bB36PjQu/z8aB32PjUdLvst5PjAEAAICGgFQKAAAAQJwYAwAAAJI4MQYAAAAk1fKJsXPq5pwqs38rndMys9yyNl/rQDin253TUue0MXq8tXN6yDnNc04TndPhZt3N2eNznNM52WO9nNOrzmmWc7rYtH3SOYVFHcPX+Zpz+pRz+kX2s3nLOW03P6vL6uJ97wvndKdzOrW+9wMNi3P6hnN60znNyD6rJ2WPL3JO3Qu0v8Q5FZztwTmdsbfPmHM6wTntdk6Xm8f+6pw2OqenorZ/yPbru+axm53Tx4tsf5Rz+nUW93JOTzmnqqxPPm3286mc59/jnIbnrLvBObU1y887py7F3i+aBo6Vvi3HSjQ8SZLUyT8p+ZaUfK3A405KDqmr1y3wes0LPHaKlBwqJRujx78sJT/L4k9LyR+y+BgpmS4lLaVkkJS8IyWHSMlXpeQKKWknJeOztpdJyc1F9qeFlMyQkmbmscFSUrkv76GOf2bNpOQIKXnyYL4u/xr2v6zfTJSSVtlydynpm8WLpKT7Pmyred53hGnTTErGS8nTUnK5efxsKblYSp4yjx0jJfdk8d+kpJOU9NnbZ1hKHpSSkVn8Syn5V7vN7P8z7GuV+P6axT8TKblGSr5R379H/jWsfxwrc/eHYyX/6uXfQUmlcE6Ds7/0/iDpTUl9nNOnndPM7C/I72btmtu/TJ3TFc7pHhPPyq7mvGja/9A5TcmuFH0+e/wc5/T37CrPzHh/kkQTJa0ssKuXSvpdFv9Z0vnm8QeSRDuTRPMlvSvpeEm7JLWV1FrSnuwv/X+R9IMiP45zJU1JEu3Zy89sUvbepkr6onMa5Jxeyt7nc86pb9buj87pIvO8rdn/h2V/oVdmP+fqK3sXZdt+wzk94JzaZI+vdE63Oac3JF2SJJorqcI5dS22n2hS+khamyTaIUlJorVJouVm/b84p+nZ522YJDmnzzinn2Xxb7MrP5OV9q9/kvSV7DP60QKv9y+SHpa02j6YJHpB0pao7S5JbZzTIZJaSNoj6T8l3Zr3ZpxTB0nHJImqzPtbal5nhmnePrtCNie7Mu2ybfzdOY3O4q3O6QfOqUrSNyT1lfRi9feVpCckXZm3PwDHygDHStSLg5ljPEzSj5JEwyU5Sd+WdKakUZJOsx/YHLdKOjtJNFLywydfkLQ6SXSipBMk/bOrGdIZLen6JNGR+7CP/SQtkaQk0U5J25xTZ/t4Zmn22P9KulzSs5K+I+lLku5NEm0v8hqnSZpW4v64JNHoJNGdkn4h6a4k0TGSHpP0w70892pJjySJjpV0rKQ3XTpk9TVJZyaJRkmao/TLqdqKJNGoJNGj2XKlpFNK3Fc0fs9JOsw5ve2c7nJO8Tyva5NEx0n6udLPWSGHSjo1SfQJpZ/pHyWJjk0SvWIbOad+Svv5z0vZsSTRbElrJE2X9KSkwZIOSRJNL/K00ZJmmeX/kfRr5/SiS1NG+pp1oyTdIGm4pIFK+3GsnaTJSaKRSaL/lLRcaV87M9vHDZJaOadupbwnNFkcK1McK1EvDuaJ8fwk0dQsPknS+OyK0y5J90s6fS/Pf1XS77O/dKv3+zxJn3VOlZImS+osaUi2bmKS6N1afQeRJNGGJNHYJNFopX9tny/pcZfmHT7knE4s8LQ+Sg/gpfijiUcr/ctcSv9S39vPa4qkf3JOt0g6Kkm0VdJHlB7YJ2Y/s3GSKsxz/hRtY7UUnBygCcs+Q8crPciukfQn5/QZ0+SR7P9pCj9X1oN7uwKU+bGkG5NEH+zD/t2QnWT/QNJ/SfpmdoL7Z+d0bYGnBH0xSfSs0pPeXyk9OXnDOVUXg5+SJFqa7U+lCr+/PUqvcBdDn8LecKxMcaxEvWh+EF9rWwltPlD6F3K11ia+VumXxEWSpjunUVnb67OhVc+lCf+lvF5smaTDJK3MhnraJYk2Oucfr3Zo1ta6VenB+NOSXlQ6bPpnSRdG7bZH76uYUt7DbmVffs6pRXWcJPqbczpT0sck/cE5fVvpz/cvSaLPlvh6rbP9BSRJ2Unt3yX93TnNlHSNpN9mq3dk/+9R/ndLqf1ytKQ/uvTboLuksc5pd5Losb090TldqvTkvL2kQUmiTzqnZ53TH5JE75mmH+qLSaL1Sk8+7s+Gl0+XtM68Nyn//b1fwkk/fQp7w7EyxbES9aK+yrVNlnSmS+/MbS7pCkkvZVdjNjinIVmuoL3jdGCSaJKkb0raoHR45llJ12fbkHM6ojoPaD89ofRAL0mfVDp0XP34lc6ppXMaJKm/zBCPS/MpeySJJijNo/pAUiIV3JfZSod599Xrkr8z/ypJL2XxIqVX8STpHySf+1ihdLjnl0r/ah4laYKks7N1ck7tnSu6L0MVDjWjCcv61xDz0LE6sKlNt0jqUGhFkmhAkqgiSVQh6SGlB/VSTopbKE15uENp/6ue2rOZ9KG7/YO+6JzOclkVCZfmHw+SDuhKWvD+XJqX3FtpnwVKwbFy33GsxAGplxPjJNFSpZ3270qHJSclif6Srb5RaSd+TeZGGEk/yq5QzZT0YpJolqRfSnpHUqVzmqU0H3GvV8Gd0w+VdpKOLi1Fc3O26m6lNzvMU5oD9fVsf6uU5irNlvS00oO0HeL9jtKbbaT0atOXlX6h/bjAyz8tfSg3sxRflPQl5zRD0ick/Vv2+M+VXk2rUjr0U33F6lxJVdkNAhdL+p8k0QqlVxMeyrbzqnK+eJxTa6VfqDMKrUeT1F7S71x6c9AMpZ+3bx3A9p6UdJnLv/muIOf0iqQHlR64ljrnb/yRpH+W9LvsyvAMSW2z741pSRKWnEoSzZHUKTsJltKD5tTsvU2UdE+S6PX9fnfp98lfXc3Nd8cr/a7bfQDbRBPCsZJjJQ4+l5YcwcHknJ6QdEOSaEF970se53Sl0isP36nvfQHqinP6iqQtSZLe0V/Hr/UTSU/Ew9kACuNYifrAzHf140Y1/ET9RNJP6nsngDr2c4X5w3VpFifFwD7hWImDjivGAAAAgLhiDAAAAEjixBgAAACQxIkxAAAAIOngTvBRUPfu3ZOKior63o3Anj01NfqbNWsWrNuxo+Y+nd27a6ouOeeCdna5TZsDKRdZ+xYtWqS1a9e6vbcsTUP8HTYl06ZNW5skSY+9tyxNQ/99rlu3Lljetq2m1r69ZyLuu61b18wV0L179zrau/3X1H6PjR2/z8aB32OoqkranVNwsnlzaeTIg7s/+6LU32W9nxhXVFRo6tSpe29YC+xBMz6RtTZs2ODjLl26BOvmz5/v47Vr1/o4Pgi3atXKxyNGjNj3na1Do0ePrtXtHczf4f7o3Vtatarwul69pJUrD+7+1Dbn3IFMsvEhB/P3+cEHNSVO4xuB4z5V7fe//32wPHHiRB/bP1bjvjts2DAff+5zn8vdp1K/J/Kesy/Pi55Ttr9HfBi/z8aB32Oo2Ffb7t1SQ35rpf4uSaVAo5d3Ury3dQAAoGmp9yvGdcmmREjhFaj4Co+9wrtr1y4fx2kQ27fXTIfeuXPngs+RpBYtWvj42muv9fEdd9xR0r4DTcEhh5T2t/mMGTWTSl1zzTXBulNOOaXg9mwflKQf/ehHBbcRX5m2V3tLvXq8P1eIAQAND1eMAQAAAHFiDAAAAEjixBgAAACQ1MhzjPPuapekP/3pT8HyLbfc4mObz/jggw8G7f793//dx2+88YaPn3/++aDdOeec4+Prr7/ex7ujOifNm9f8CvbnbnigsZgzZ06wvMrcGdmzZ08fT548OWh36623+njTpk0+ju8PuOeee3z88ssv+3jChAlBuxtvvNHHLVu2LGnfAQCNA1eMAQAAAHFiDAAAAEhq5KkUxdgUBknq27evj2+++WYfjx07Nmj317/+1ccLFy7M3f5dd93l41JnuSF9Ao3dtGnTguXHHnvMx8uXLw/WnXbaaT7euHGjj7t27Rq0O+KII3y8evVqH8epFCPNlEw7d+70cceOHYN2tqTimDFjfHzkkUcG7Rri7HkAgAPDFWMAAABAnBgDAAAAkso0lSKetc6mINgh0unTpwft7HDs+++/H6ybN2+ej2fNmuXjp59+OmhnZ7vr06ePj99+++3c/Z07d66Pd+zYEayzKRx29rxevXoF7UqdIQxoaGyVh7PPPjtYZ9MRbEqEJB199NE+XrRokY/vu7s4UjUAACAASURBVO++oN3xxx/v46FDh/o47mtPPPGEj88//3wfxykSkyZN8rGtNmMfl6SPf/zjPh4yZIgAAOWPsy0AAABAnBgDAAAAkjgxBgAAACSVaY5xsbJmb731lo9ff/31YJ3NYbS5iJJ07LHH+njZsmU+3rp1a9DOlpcaNWqUj9euXRu02759u4/btWvn43Xr1gXt3nnnHR/bWbZatGgRtKM0FMrJzJkzfWxze7/3ve8F7Wwpw7iE4sCBAwu227BhQ9Dus5/9rI8XLFjg4/feey9oV1lZ6eOTTjopt53N++/Xr1/B50vSD3/4Qx///Oc/FwCg/HHFGAAAABAnxgAAAICkMk2lKMYOsw4ePDhYZ9MievToEazbvHmzj7t16+bjOIVh6tSpPp4yZYqPbWkpSVqzZo2Pt2zZ4uMuXboE7exr2ZJsNhUDKDd2hjs7W+S9994btHv88cd9bPuCFJZRmzNnjo+ffPLJoJ3tu7as26pVq4J2Nm3JlkO05RSlMB3DzrI3fPjwoN3HPvYxAQAaF64YAwAAAOLEGAAAAJDUSFIpbIqETVuwM9NJ4d3xI0aMCNbFM+FVa9++fbBsZ9az6Q5xFYk9e/b42FbRaNu2bdDOLtu74+M75YFyMn78eB8PGDDAx7b6iyR16tTJx3Ffs+lJixcv9nHcr8866ywfz58/38d2JkkprJRhU6TilAubZhFvw1q6dKmP46o0VJEBgPLEFWMAAABAnBgDAAAAkjgxBgAAACQ1khzjjRs3+njHjh0+7t27d9DO5hLacmpSODtds2bNfNy6deugXceOHX1s84qTJAna2dJTNo/ygw8+CNrZZZu/HOc22vfVqlUrAQ2ZLaG2ZMkSH48ePTpoZ/OF4zz/zp07+9iWYYzz+YcMGeLjTZs2+TjO57dl2ey9CPZ1pLDPjxkzxscPP/xw0M6Wf4tntCTHGADKE1eMAQAAAHFiDAAAAEhqhKkULVu29HE8RGpnnbOpCfE6mxZhZ6OTwuHeNm3a+DhOubDtbFm3eHjXpnfs3r07d9/t0HQ8ax/Q0OSlQTz99NNBO/tZjmd7tKlQdkY7G8fLdoY8O2udFM5o9/nPf97Hy5cvD9pVVlb6+KWXXvLxa6+9FrSzfTn+PgEAlCeuGAMAAADixBgAAACQxIkxAAAAIKmR5Bjb3ESbY2zLrsXt4ilcba6jzSu20znHmjev+fHZKaClsAybLa9mnyOFucnxurx2QEN3/PHH+/iaa67xcZyna/N+169fH6xbsWKFj22esp0CXgrvMbBl2OI+aUuq2emcbdk1KZyO3X5PxKXmbB51nM+MEvTuLUXTcXu9ekkrVx7c/QEAccUYAFAf8k6K97YOAOoQJ8YAAACAGkkqhS2NVmzWOlsazQ7NSuGwqB1ytbPRSWG6g30tm8IhhekYNq3CzpwnheWgRo0a5eM4hSOeWQ9oSGbOnBksP/DAAz6+8sorfRzP/GhLFNoZIiWpffv2BdfFfbLYjJFW3myUcQqT7bu2X19wwQVBu5VmqP/FF18M1l111VW5+wE0NPHMjTbFyaYWSdK7777r46OPPtrHd999d9DO9oG+ffv6OO7ntlSqFX9XxKVT89hjZbFUSCAPV4wBAAAAcWIMAAAASGokqRR2KNXORhWnH9jZ4+ysWlJ4Z7sdfomHb+ywqx3qiYdsWrRo4WM7XBx76KGHfDx06FAf26EnKUwXARqabdu2Bcs2zeC3v/2tj+OZ72699VYf28+/JPXq1cvHNkVi2bJlQbtTTjnFx7a/9uzZM2hnK0cMGTIkt51Ns7rssst8PHv27KBdVVWVj4877rhgHakUqAt5KXV5KQNxZRab/jd+/Hgf33nnnUG7+fPn+zju2za9aNCgQT5eFd0wOWbMGB//7Gc/8/Hzzz8ftHviiSd8fPLJJ/u4WOqEPebHaYykT+BAccUYAAAAECfGAAAAgCROjAEAAABJjSTHeMeOHT62pV/ifKw5c+b4OC7lZmenszPkxTlall0X50PZ/GNbdir26KOP+vjf/u3ffBznTcWzfQENyfDhw4Pl2267zcfnnXeej+0Mk5L08MMP+zgu43TooYf62Pav+++/P2g3cOBAH9vcSDtzniS98sorPrbfE0uWLAna2dnzrLFjxwbLZ555po/j9w/UpVJLmcWzv06fPt3HP/7xj318xBFHBO3GjRvnYzuLpRSWNrX3DEycODFo96tf/crHHTp08LG9d0AK8/gHDBjg45tuuilod8kll/g4Pj4CtYkrxgAAAIA4MQYAAAAkNZJUCluexc4sZ1MsJGnRokU+tkM7cVtbGs2WXZPCISsbx0NWVrEScrZsnC1DdcwxxwTt4qEzoCF55513guW3337bx7ZvrF69OmhnSxnGaUs2pcluI059ePPNN31s06Xi/m/7ni3/ZmfykqT169f7+KijjvJxPARs3/OMGTOCdXH/BWpD9bGu2PGmGJsWYWe7s6UM98U111xTMI4tXLjQx9/+9reDdZWVlT62KYM2HSveRp8+fXxs+6sU9vP4O8UeR/O+DyTprLPOKvAu0FRwxRgAAAAQJ8YAAACApDJNpYhngbPDJba6hJ3pLvbee+8Fy+3atfOxnd0uTqWIh1yqxbPb2fQOewdtPGvX8uXLfbx06dLc/SWVAg1ZnEphq77YPvPnP/85aHf77bf72KYtSOHd7/bzb1OTJOlTn/qUj994442C+yCFQ7EXXnihj+3MeVI4NPuVr3yl4Lal8Dsk/p6wM2na9wHsr507d/pjhE1VksI+0aZNGx/H1SpuuOEGH9tUo9deey1oZz+/8fHW9md77J0yZUrQzs5+aVMchw0bFrQ799xzfWxnpLRVaSTpscce87GtMBOnRdq+GB837XHarrPvV5JOOOEEoeniijEAAAAgTowBAAAASZwYAwAAAJLKNMe42Gx0NpcpzsOybB6WFOYm2+3HM87ZMjk2R6nYzHc2r6lfv35BO1sCKs7TtGzOcvz+97d0D1Bbpk2bFizb8k+2LNTcuXODdjaff/z48cE6OxuX7YcvvfRS0G7UqFE+tn0+zhu0+3H66af7OJ6xy94TcPjhh/s4zjG2fXnt2rXBujVr1viYHGPUhmbNmvlZVOO8X1ty0N6rEh+XRowY4eNf//rXua9l84/jWebs/Tg9e/b08Sc/+cmgnZ3FzpZX21/XXXedj+39Q/Y7RPrw/T6WLdEWl0616LNNG1eMAQAAAHFiDAAAAEgq01SKmB0usmVrpk+fnvucOJUib5atuAxTXtpCPJxj96nYkE310Jj04WFmKy81o9g+AQdLXPLs5JNP9vGsWbN8/JGPfCRo16VLFx/PnDkzWLdz504f581YJYWpRbb/23SGuJ3tQ3EJRjt0bPtnXP7RDktv2bIlWGeHmIHa0KxZMz/EP3bs2Hrem/plUyaB2sYVYwAAAECcGAMAAACSyjSVIh76tMOstnqDncEqFs+Ws23bNh/bIdy4AoQdZi1296tNb7BpGnFqRrdu3XycN6ueVHpqBlAfKisrg+XBgwcXXBdXZVmxYoWP41kh7Z3sNlXB3oEvhXfh29nt4pkk7Ux1q1atyt2e7ZNDhw71sf1ekMKZuRYvXhys27Bhg487deokAEB54IoxAKBs9e4tOVf4X+/e9b13AMoNJ8YAgLJlLv7v0zoAKIQTYwAAAEBlmmMc5/rZHGNbNs3m9sbivL+VK1f62ObzxjPf2RmBbDub2yyF+cJ2/+IyM3Y/4lxHy74vuz2gIXjqqaeCZZsH/5Of/MTH559/ftDu+OOP93E8S9dxxx3n4yVLlvj4xBNPDNodddRRPrZ9I+7j9p6AkSNH+ji+F8GWkLMl37761a8G7Wx5xTg/+utf/7qPKyoqBAAoD1wxBgAAAMSJMQAAACCpTFMp4nJlcRpDNVsKSpKGDBmS+xxbXs2mLcQz2tllW8qtWAm1eIjYOvLII308Z86c3HakUqAh++///u9g2c6EZ9ORBg0aFLTbuHGjj+Nyha1bt/Zx9YxfktQ7KjVgS8DZvrF8+fKgnZ25zvb/ww47LGj3/vvv+9imbX3+858P2tlZ/OI+Gc/wBwAoD1wxBgAAAMSJMQAAACCpkadSxFUe7ExV8TZstQmbIhFXtsibZS8eSrXrilXHaN++fcHXjWfcs6kexWbcA+rDggULgmWbBmE/y0cccUTQ7oUXXvDxI488EqybPn26j21axG9/+9ugnZ1lzlavmD17dtDOpkjY7cWz9q1bt87H5513no9thQopnD0vrpRjU0R69OghAEB54IoxAAAAIE6MAQAAAEmcGAMAAACSyjTHOBaXeaoW5/YOHjzYxzZnV5JatWrlY5sfHLez6+K8Qit+Xp527doV3N/33nsvaGfLtRV7XaA+bNu2LVi2+bg2Hj16dNDOzm5nyylKYcmzqqoqH9v8ZUm64oorfPzmm28W3LYU5jp/6lOfyt0nOxPeBRdcUHDbUliGLn7/xe4rAAA0XFwxBgAAAMSJMQAAACCpTFMpbNklKT9tYdGiRcHyqaee6uOFCxcG6+wseW3atPFxly5dgnY2bcMOzcYl1Gy7vFSP+LU2bdpUcNvSh2fgAxqSLVu2BMu2bNq8efN83LZt26Dds88+6+P4M2/71MqVK308fPjw3P2w2x8xYkSwzpaUszPp9ezZM2hny7DZ7wVbWlEKy0HG7z/+jgIAlAeuGAMAAADixBgAAACQxIkxAAAAIKlMc4zjfN686WfjPD9blimeErply5YFt2FLN0lhnqGdBjou12RzDg85pObvj3ifbEmp3r17+9jmaErhVLrFcpaB+hDn85588sk+fvvtt33cokWLoN3mzZt9bPugFObcT5w40cfdu3cP2j3//PM+tiXUBg4cGLSbPHmyj88991wfx33N3pswdOhQH48ZMyZo99Zbb/m4Y8eOwbpBgwYJAFB+uGIMAAAAiBNjAAAAQFKZplLY2efi5eXLl/s4niHu8ssvr9sdM7p161ZSO5veYYeBx48fH7SzQ9Vx2gZQ3w4//PBg+YUXXvCxLWtm04okacaMGT7u27dvsM7O/mjTG7p27Zq7HzbNKp59zi7bVKd4lkmbWmFTruzsmFJY1q1fv37BurjMIwCgPHDFGAAAABAnxgAAAICkMk2lWLx4cbBs717fuHGjj7/5zW8etH2qDf/6r//q4wEDBgTr7MxfthqGxLAt6l9cleLOO+/08ZQpU3Kfd/XVV/t40qRJwTo7o6VNM4rTlObPn+9jW/UiTpGwyzalI065sv1p2LBhPrZpH/FyRUVFsC5O9wKA/5+9O4+/qqr3P/76CMookyCQE4rihICKY46VZqWZafnTyul2y7yOleW9VqaV2fVWV+2qpZlN5lBqODQ4kWTOzIgDKiqCKCpOIJOf3x97fTefvTjn8AW+yJfv9/18PHjwOXuvs/c+wz57fdf67LVk7aAWYxERERERVDEWEREREQFUMRYRERERAdbSHOM4+xxUZ4KLM1Dtt99+zd5mHJZpTeUHHn744WWczwIWZ+MTaW06dqz+lHz6058u4zijY27o0KE149wJJ5xQxjvvvHNlXTz/45Bved7vwIEDy3i77barW+6QQw6peQz5fmOe8iabbFJZpxxjEZG1k1qMRURERERYS1uMRdaEs846q+66Cy644H08EpE1R+eBiLRlFlMI1sgBmL0CPLfcgtKSNnP3fi21MX2Ga5w+z7ZBn2Pbos+zbdDn2HY067Nc4xVjEREREZHWQDnGIiIiIiKoYiwiIiIiAqhiLCIiIiICtPKKsRkDzLjWjKfNeNSM280YshLb6WXGSQ3Wn2bGZDOmmHF6WD7cjPvNmGTGLWb0SMs/aMZEMx4xY6uwj7+b1X9PzfijGVukuLsZPw+vbbQZu63oa0vb+q8Qr2fGvWYacURWA7MBmF2L2dOYPYrZ7Zit8DmJWS/M6p6Taf0fMXscs6mY7ZGt/xpmjlnf9PhwzKZgNgazDdKywZhd12AfhtndmPVIj89O25iI2XjMdkvLp5f7qT7/k5jVHqLBbD/M9gyPT8bshJplRQAzzk7XoIlmjF/Z60GN7Y42Y+TKlDHjZDOmmeFm9A3LzYyL07qJZuwU1h1rxlPp37FpWScz/pqusyeFsr+Iz62x/0+Z8Z0Ub52Oc7wZU834xYq9E3X3sZ8Ztza3jBkHm3FeS+xbWqdWWzE2w4CbgNHuDHZnZ+A/gf4rsbleULtibMZQ4N+BXYHhwMFmbJlWXwmc5c4O6VjOTMu/BnwcOB04MS37FnC+O+/V2c/2QAd3ngnbfg3YKr2244FlL77NU1aM3VkI3AUcuZLbEqmtmLXiJmA07oNxXy3nZHIR8Ffct6E4L6eG49gEOBB4PpQ/BdgF+DlwdFr2fYrzsp6PAxNwfzNVvA8GdsJ9GPAR4IWGr8B9FO7Ljk9m1hHYD9gzLL0qHaPIMswov3/uNO/79/64j+JY8lEUPgZslf59CbgMwIw+wDnAbhTX1HPM6A18FPgnMAz4Qio7nOKaOLbB/r8BXJrii4GfujPCnW2BS1b51a2c24BDzOi6hvYvq1mrrRgD+wOL3Lm8aYE7E9wZk/5avTD99TnJrKgEplbYu8wYm5Yfmp56ATA4/aV5YbafbYEH3ZnnzmLgH0DTtF1DgHtTfAfQNDXdIqBr+rfIjMHAJu6MbvB6Pgf8OR3nYIofjm81VaTdedad29L6r6bXNjlrwb45tS5PMeNLadkFQJf02n6fit6c9ifSkvYHFuFenpO4T8B9TGp9vRCzyZhNwqz4w8ysO2Z3YTY2La+ck6lltnpOmvUE9gF+mfaxEPe5ocRPKS6YcUid94BOpHMSs72Bl3B/qsHrKc9JYCAwB/cFaZ9zcJ8Zyp4SXsM26TiPw+xnKb4as8sxexC4nuIP5jPS69sb93nAdMx2bXA80n4NBOa4swDAnTnuzAQw4ztmPJyuB79IjUZNrbw/MuMhM540Y++0vEvqaZ1qxk1Al6admHFZ6umcYsa5yzsod8a5M73GqkOB37jj7jwA9DJjIEUF+A53XnPndYrr5kEsvWauCzRNC/k94Nv19p16hxe4Mye8RzPCsU1K5QaZMSZd98eaFX+Qplbe0amn9nEzfh/eu4PSsrEsvd5jxq5W9BKPM+NfZmxd4z1xYDTFHzLSFnn6Zre2f+Cngv+0zrrDwe8A7wDeH/x58IHgHcF7pDJ9waeBG/gg8Ml1trUt+JPgG4B3Bb8f/JK07l/gn0rxV8HfSvEI8AfA7wHfGPxa8K2W83r+Ab5Dij8JflOdcjuDTwLvBt4dfAr4jmldn/R/F/DJ4Bukx29n2+gA/sqa/gz1r439g1Mdap6TDoc73OHQwaG/w/MOAx06OvRIZfo6THMwh0EONc9JhxEODzlc7TDO4UqHbmndoQ4XpXi6Q98UH+DwqMMtDj0d/u7QZzmv5zmH9VPc3WG8w5MOlzrsG8pNdzglxSc5XJni4xx+luKrHW516JAef9fh69n+znb42hr/HPWv1f1Lv/Xj07XoUvB9w7o+If4t+CEpHg3+4xR/HPzOFH8V/KoUDwNfDD4ybitdI0aDDwvbGtng+KaD9w2PbwXfKzy+C3wk+NfBvxWWfzst6wh+Dfg48KPTNfC7y3lPjm96feHxG+B/AT8DvFda3hW8c4q3An8kxful8huDr5Ou7XuBdwZ/IZU18OvBb03P6QHeMcUfAf9T2Nat4Vg+11RP0L+29681txg3shfwB3eWuDObopV3F4q/RM83YyJwJ7ARy+nmdWcq8CPg78BfgfHAkrT6BOAkMx4F1gcWpueMd2d3d/YHtgBmUXQ0X2fG78xq7nMg8EozX9tN7rzjztvAjVC0BACnmjEBeADYhKIbq9ZrWgIsNGP9ZuxPpCXsBfwB9yW4L3NOYtbsc5JiRs6dgMtw3xF4BzgLs64UaUPfWeYZ7nfgvjPuh1C0Zt0ODEl5ylek5+b64P5Wev7bwM4U3cKvANdhdlwoe2P6/1FgUJ3jvgH3JXXWAbwMfKDBemmn0m995ftnxnFp9f5mPGjGJOBDwPbhqbW+l/sAv0vbnQhMDOU/m1pJx6XtbNfiL6YGdxa7c7Q7OwI3UKQh/tiMn6QW3U/WeFrlmunOryh6eG+gSFV6wIxOFK3QV6T35waqr+khd2Z40TM7nuI92gZ41p2n3HHSe5X0BG4wYzJFz1R8ryOdy21Ya64YT6H4oVgRnwP6ATu7MwKYDXRe3pPc+aU7O7uzD/A68GRa/rg7B3qRA/wH4On4vNQt8y2KLqFzKLp3rwBOrbGb+eFYpgDDzejQ3Bdmxn4UuV57uDOc4oet0WvrBLzb3O2LNMMqnZO4N/ecnAHMwP3B9PiPFBXlwcDmwATMpgMbA2MxG1A+s6gAHwf8H3AucCxFbmOt1KLFmC39DSwq9aNxPwc4maWpU0DRxU3xR3O9G1vfWc7r6kzxOyCyjNTQM9qd8vtnRmeKHNsjvLjX5Qqq509zvpcAmLE58HXgw17kMd9GM66PdbxI0TjTZOO0rN7y6CTgN8DuwBsU98N8rcY+4jUTAHdmunOVO4cCi4GhwBkUvyvDgZHAeuEpC0K83PeI4lp+jztDgUPy/Qc6l9uw1lwxvhvo1JRLC2DGsJRHNQY40owOZvSj+Av5IYq/9l52Z5EZ+wObpae+BfVbT83YMP2/KUW+0TXZ8nUoKsCXZ089Brjdndco8qfeS/9qtU5NheKmPneeBh4Bzg05T4PM+ER6bZ8yo6sZ3YDD0rKewOvuzDNjG4oflSaLzFg3vJ4NKPLVFtV7zSIr4W6gE2blOYnZsJTPOwY4ErMOmC1zTuK+CLPmnZPuLwEvYNaU3/dh4DHcJ+G+Ie6DcB9EUYHeKZVvciZwMe6LKHIrnfrn5BMUPT5gtjVmsQdmBKs2bWut1zcEmLwK25Q2yooRF2p9/5oqZnPM6A4c0YzN3Uu6ATXdXD4sLe9B8cfbG6lX82OrcMijgGPS/T67A2+4Mwv4G3CgGb3TTXcHpmWk4+lNkZv7G5ZeM52QBx2U18z03IOarnNmDAA2oKh09wRmpVbhL8ByG5weBwale30AjgrrerK0In9cg23oXG7DWm3FOHVxHAZ8xIohzaYAPwReorgzfiIwgeJi/Q13XgJ+D4xMXSrHUJwAuPMqcF+6eSG/+Q7gT2Y8BtwC/Ic7TTf6HGXGk2k7M4FfNT3BijtSj6NomQL4CUX37f+ybAUair/O9wuPv0jRpTwtddtcTVGpH5vih4AHgSvdGUeR5tHRjKkUNy49ELb1C2BiuPlu/7Q/kZZTJNcdBnyEYri2hudkqrD+HhiJWeWcxP1V4L50s16tc/IU4PcpBWMEcP5yj8/sA8CuuN+cllwCPExxI9w1NZ4Rz8nuwK8xeyztczvgu8vdZ323AIeVN98VPkhxM5JIrjvwazMeS6mA2wHfTdeiKygqYX+j+D4vz2VA93StOI8izQJ3JlD0ND5OcT7ct7wNmXGqGTMoWn4nmnFlWnU78AwwLR3fSWkfr1G0uj6c/p2XljX5DvCDVIn9G0Wa4CTgtzV2fy+wY1PjEUUle3JKJ/wbcGa67l8KHJuWb8Nyem7ceZciZeW2lFbyclj938APzRhH49ZlXWPbMCuudbK6mdEFuAf4YMoBXp37upFimLknV+d+RNZqZgOB3+B+wPuwrx2Br+L+hdW+L5E2woyLgFvcuXNNH0uT1Np+jTsfXtPHIqtHq20xbmvcmU+Rh7zR6tyPGesBN6tSLLIc7rOAK2ia4GP16kuDoalEpKbzqZ0GtSZtSu2caGkj1GIsIiIiIoJajEVEREREAFWMRUREREQAVYxFRERERIDlD3a92vXt29cHDRq0pg+jXZk+fTpz5syx5ZdsHn2GS02YAIsX117XsSMMH97y+3z00UfnuHu/ltpea/w8X3jhhTKeP786rn6fPn3K+L333itjs+pX/PXXXy/j/v2XTr7Xs2fPFjvOVdEePsf2RJ9n26DPse1o7me5xivGgwYN4pFHHlnTh9GujBw5skW3p89wKWvw58bixbA63iYzW5WJKJbRGj/P0047rYwnTZpUWfeFLywdAe3tt98u444dqz9vN954YxnH7R188MHNOoZY6QZYZ52W7XBrD59jNGAAzJ5de13//vDSS7XXrS3a2+fZVulzbDua+1mu8YqxiAjA6NGjK48vvfTSMu7UqVMZv/baa5Vyp566dAb2Dh2WTnrVtWt1lKfdd186WeT1119fxqNGjaqUu+CCC8o4tka3dEW4vatXKV7eOhGR1Um/9CIiIiIiqGIsIiIiIgKoYiwiIiIiAijHWETeR0888UTl8Y9+9KMyfvLJ6izmw4YNK+OpU6eWcZcuXSrl+vbtW8Zz5swp46FDh1bKxVEp4o15MX8Z4PTTTy/jLbfcsoxPPPHESrkNN9wQERFpW9RiLCIiIiKCKsYiIiIiIoBSKUSkBSxZsqTyOA6bdtlll5XxAw88UCnXrVu3Mt51110r67p3717G7777bhk//vjjlXIxtSKmN+TH9PDDD5fxv/3bv5Vx7969K+XefPPNMp41a1YZf/nLX66Uu/zyy8s4ThgC1TGPNcybiMjaQ7/YIiIiIiKoYiwiIiIiAiiVQkRaQEydyMUpnAcMGFD3efkUznEUiU9+8pNl/Nhjj1XKxXSHH//4x2V83nnnVcodeOCBNfcb0zSgOmNejx49yjifEvqaa64p4zPOOKOyTukTIiJrJ/16i4iIiIigirGIiIiICKCKsYiIiIgIoBxjEVkNYn5wzOHt169f3XKLFy+urFt//fXL+JVXXinj/fbbr1Ju9uzZZXz99deX8eabb14pt80225TxO++8U8YLFy6slFu0aFEZx6Hg8vzoGTNmlHGj4epERGTtoRZjERERERFUMRYRERERAZRKISKrwbPPPltzeT402oIFC8o4Tz+IM989bKcwwwAAIABJREFU//zzZRxnpgMYOHBgGcf0iZdeeqlSbvr06WUc0zTyWevMrIxjisRbb71VKRdfyxtvvFFZ16dPH0REZO2jFmMREREREVQxFhEREZFmGDAAzGr/y+5PXmsplUJEWtyLL75YxjHlIE9viCM95CkSU6dOLeO5c+eWcZzpDqojR8Ry48aNq5Tr27dvGccRKl544YVKuZg+8fbbb9c81tzjjz9eebznnnvWLSsisrYKgwCt0Lq1iVqMRURERERQxVhEREREBFDFWEREREQEUI5xyd1rxuuss+p/O9x7771lvM8++6zy9porzu4F0K1bt/dt39K+xRzjTp06lXH+nYyz3W2wwQaVdc8991wZxxnyOnfuXCkXt7/hhhuW8bbbblspt+6669bcRj6E3JAhQ8r4zjvvLOM4fBxUc5anTJlSWaccY5Ha4vUVqvcMfOADHyjj/LfiJz/5SRmffPLJZZxf19Zbb726+473D2h2SqlHLcYiIiIiIqhiLCIiIiICKJWiFGe7inEjp556ahnHmbkA9t577zK+6667yjjOzAWwySabNGtfscu5Y8f6H9uFF15YxjfccENl3d133w3Ae++916x9iqysmJ4QhzybNm1apdz8+fPLeNCgQZV1MbUipkG8+uqrlXIxzWLevHllnM9Ut8UWW9TcXt6lGmexu//++8t46NChlXIHHnhgGeevS6S9yVMk4nX0mWeeKePTTz+9Uu7EE08s47Fjx5bxaaedVil33XXXlfFtt91Wxtdcc02l3MEHH1zG+dCOXbt2LeMvfelLZZynceWvRdoXtRiLiIiIiKCKsYiIiIgIoIqxiIiIiAjQxnOM81zalckjjrlRALvssksZH3300WW80047VcrFvMWYv3TKKadUyt18883NOo5GecW//e1vy/jaa68t45jbCUunrc2HpxJpaXF65zjsUv6djDn3+brBgweXcRyS7aGHHqqUe+WVV8p4u+22q7u9RYsWlXHMbY55h/kx/fKXvyzjs88+u1Iu5jPnQ0uJtDeNrqkxv3/UqFF1y914441lfMABB1TWxSERFyxYUMb5fTr/+Mc/yjgf2jFqdE2V9k0txiIiIiIiqGIsIiIiIgKsRakUcfiUvMum3rpGs9YtXLiw8vill14q4x133LGM86FlvvnNb5bxsGHDynj69OmVcrFrNc7AFWfSAujdu3cZ/9d//VcZf+pTn6qUi8NL/fOf/6ysu/TSS2uWGz58eKXcRhtttEwZkdUhng8xDSIfGu1zn/tcGV9wwQWVdfF7Gs/lmKYB1eHbXn755TKeMGFCpVw8X+PsWHEoRKgO8xaHkMtTLmKqhoZ3EqmvaahQgKeffrqybtNNNy3jq6++uozzmStj2mGc7S6vD8Qh2vbaa6/KurjvW265pYw///nPV8rFGfKk/VGLsYiIiIgIqhiLiIiIiABrUSpFozte660bM2ZM3eecc845lcdNaQZQvRM9H9lixowZZZzfHR/Fu95jN+snPvGJSrmePXuW8WWXXVbGV111VaXc+uuvX8Zz5syprItdUXvssUcZP/jgg5VyTV3Q6iaS1S12Z/bt27eM586dWykXz5Otttqqsi6mODSNqALLpkHFcyimcMycObNS7oMf/GDN5zz33HOVcvFci6PS5DPpxTve8xEw4ogVeQqGyMqql7ITr4GxTH79ylOZ6onnXhzNpdE2YnoSwA9/+MMyjudRPlLEgAEDyvjnP/95GccRoKB6Hn3oQx8q4z59+lTKxVTDOGINVNMz/vSnP5VxnkqhESvaN7UYi4iIiIigirGIiIiICKCKsYiIiIgIsBblGDcybdq0Mo45jH/4wx8q5WKe4re//e3Kuji8Why6LZ/RKuZexVypPG835nbFmebijD0An/nMZ8r4k5/8ZBk/8cQTlXJxmJl8pp+PfOQjZRxzJ6+77rpKuaYcsObO+ifSXHneb3wch1rL823j4zxPN57Lm222Wc3lUB2iLW4jDrsI1fMwlovbhupwcN27dy/jPJcx5vrHPEmo/obEWb9EVkVzfrsblWlOjjJUc2ybm28bh1qDar7/DjvsUMb5NTXODDtw4MAyjvfzAJx00kllPHv27DLeZpttKuXi9bBHjx6VdSeccEIZx9+N3/3ud5Vyec6xtC9qMRYRERERQRVjERERERGgFaRSLFiwgKeeegqAa6+9trJuww03LOPYDZoPmxSHk4ndnfvvv3+lXBz+JR9qLXb3xu6XfGiamDLx2muvlXHsOs2PMQ5JladSxHWx23brrbeulIsz+MTZ8vLjiLMDxS4qgClTpgDV91KkJcR0JqjOdhfPzzfeeKNSLnad5l22MW2pS5cudbcRZ76L5/yTTz5ZKZcPc9gkT++I53w8hjiMW/44HgMs+xsl0hJWdIbF5g7Plovf+8svv7yybty4cWUch2I87rjjKuXikGrXXHNNGT/22GOVcvH3Yc8996x7TP/3f/9XxmeccUbN44Hq9TsO0QjVoU1j/Mgjj9Tdr7Q/ajEWEREREUEVYxERERERoBWkUrz88svljG8TJkyorIvdsVHe5RpHYogz3eRdrjE1o1u3bpV1zz77bBlPnjy5jPM7Y+Md8TENIk9PqDe7XP6aYhfxyJEjy/jhhx+ulPvZz35WxjHtA2D77bcv43h3cV5uyy23rHkMIqsqHymiXirFsGHDKuXiaA75uRZTi+IoEvm+4nc+bq8pRavWccQu6TgKBVS7kfv161fG+flULw0Klv3tEWkJKzqiUH4diqkVMe0uP/diekI+Wsyxxx5bxv/4xz/KOM4qB9XZ7uJ1Ob9WxutyI/G1xxEl8tcYZ53MR8o48MADyzieszGtAuD5559v1jFJ26QWYxERERERVDEWEREREQFUMRYRERERAVpBjnHv3r054ogjgGVnlnrhhRfK+PXXXy/jfCikmTNnlnHMN44z7+TrYk4xVGfjiTnMeV5h3EYc5inO7APVYaPiUE433nhjpdzf//53miO+5phDlYu5000z3TVpyhVr7kxGIs0Vc/6g/lBr+QxxMe83zz3s379/GcdhDvPvbyx39913l3E+LFScgS4OeZjvNx5vzMnMz6eY8xhfB1Tzj0VaWqNh2+Ksq42Gaxs/fnwZ5+fAuuuuW8ZnnnlmZV2cUTJeb6ZOnVopF/PzY85yfuxx1rkTTzyx7vFG8Xx77rnnKuuGDBlSxvk9DTfddFMZf+ELXyjjESNGVMpNmjSpWcchbZNajEVEREREUMVYRERERARoBakUXbp0KYcb22yzzSrr4qxYUT48S+wGikPE5N27f/nLX8o4n6UndrnEGePy7tNVdcghh1Qe//Wvfy3j4cOHl3GewhG7xPKhoWLXVEwJmTVrVqVcUwpG3u0rsqryWeXirHDx+7b55ptXysXu13xYqJg+EVMwYooVVNMWYjpWTImAardvXBfTPqD+cIb5eRPL5d3Dml1SVoem71m94UChmmqUD0X49NNPl3FMQchTAWMa0je/+c3Kuuuvv77m9jfZZJNKuXgdveeee8o4zkAL1et0TIWKM+fl4rVy9uzZlXVHHnlkGefX24997GNlfPTRR5dxnp6p87d9U4uxiIiIiAiqGIuIiIiIAK0glaJDhw7lSA95t89dd91VxrG7M94xC9CrV68yHjp0aBnnozecfPLJZRzvUAdYuHBhGcdu4byLJYpduPld6LFrNXZtbbTRRpVysTt2zJgxZRy7iqDajZvfaRy71eJrzrumY9eWSEvKv/+dO3euua5v376VcrEbNY74AtWUoTjbXT4qRUwfiikXr732WqVc7B596aWXyjj+fkD9cz5PuYiP82OKvyciLaVpJJTmjiyUp/j8+c9/LuMnnniijPPUgThiRZwJFqqjLMUZ7UaNGlUpd/rpp5fx6NGjy/jcc8+tlIvn4ve+970yzlMp4mySjWbLi9vLxWOK4qgZsOxoFtK+qMVYRERERARVjEVEREREAFWMRURERESAVpBjHOXDveSPm0ybNq3yOOYwPvXUU2Uc8xKhOgRazK+C6tBQPXr0KOM8nznOdhVzIPNZ+2JOcMzzyvOf4uxAcV9x9qJ8G3EWwFwcJis/psGDBwPLDmMl0tLi9z/m4uZ5ulOmTCnjfIjC+Diey/EchOosdnG/+bkbv/cxtz/P2Y/5wfF8ze9LiPKcz0azU4qsjHfeeYf7778fgMsvv7yyLt5P0mjm1rguXivyoU1j3n0+7OcDDzxQxnEI1HgNzcXc/5grnIv5y7vttltlXbzOH3DAAWUcz3+Aa6+9toxPO+20yrqtttqqjHfaaacyzmfPu+iii+oeo7R9ajEWEREREUEVYxERERERoJWlUjTXlltu2axy+Ww+IrJ65OkNMY0hphzFme4A9txzzzLeZpttKutiGkNMd4hDREG1ezgOXZjPDhbTLGJ3bj6kVZztMqY05TPfxWOKw9PBsikjIquqS5cu5TBiX/ziFyvr4jkRU+3yYRTj4zhEW14ufre/9a1vVdbFcyKmHebDgcYh0GJqxte+9rVKuZhO2Cjl4gc/+EEZz5gxo4zzGXLj+Zyvi+lUcSbM/LdH52/7phZjERERERFUMRYRERERAdbSVAoRaV3ydISY3hDTLPKRUr7yla+U8TPPPFNZN3bs2DKO3a2TJk2qlHvsscdqbj9PpYjdtDHVY+bMmZVyxxxzTBnvvvvuZZx37ebHEeWjAYisqnXWWafs/t97773X8NG8/+IIGCKrk369RURERERQxVhEREREBFDFWEREREQEUI6xiLSAfLi2KOb67rXXXnXL5TPL1Ztpbt999627jTiUVD4T16rO+BjznKHxa85nrhQRkbWDKsYiIiIi8r4566yz6q674IIL3scjWZZSKUREREREUIuxiLSATp06VR7XSzOIw6Tl8uHV4uxbcTi4RikMcZi0lU2dqLev9ddfv+7x5akTCxcuXKl9i4jImqUWYxERERERVDEWEREREQFUMRYRERERAZRjLCItYM6cOZXHixYtKuOYixunil4RMdc3n366Uc7xyoj5wvHY8xzjOBxcvq5RLrWIiLReajEWEREREUEVYxERERERQKkUItIC8qHWYirB4sWLy3jgwIGrvK/mpk40SrloNPxbvVSKfPi3mC4SXyMsm1ohIiJrB7UYi4iIiIigirGIiIiICKBUChFpAXHGOYC33nqrjOfOnVvGecpFlM8eF9MYVkajlIuVGckiH1EjvpZ8FIpu3bqt8PZFRGTNU4uxiIiIiAiqGIuIiIiIAKoYi4iIiIgAyjEWkRZw/PHHVx4/+uijZRxzjHfeeee621jZWfFaWp4v3SQfai4+zo+9V69eLX9gIiKy2rWOK5GIiIiIyAo466yz6q674IILVmqbqhiLiEizrY4LkYhIa2H57FDv+wGYvQI8t0YPov3ZzN37tdTG9Bmucfo82wZ9jm2LPs+2QZ9j29Gsz3KNV4xFRERERFoDjUohIiIiIoIqxiIiIiIigCrGIiIiIiJAC1aMzTjbjClmTDRjvBm7tdS20/b3M+PWFtzeVWa8bMbkbHkfM+4w46n0f++03My42Ixp6TXulJZvbcajadkeaVlHM+40o2uD/f+vGfuk+GAzxpkxwYzHzPhyS73OsL+3V/H5dza9F9J+mTHAjGvNeDp97283Y8hKbKeXGSc1WH+aGZPTb8rpYfkIMx5IvzGPmLFrWn54KjvGjA3SssFmXNdgH2bG3Wb0WJ2vzYx+Zvx1Rbcj7dfqup6aMdqMkStTJp1b49O/mWbcnJafGZZPNmNJuo72M+Ofadmnwnb+bMYHGuz/dDOOSfHuZjyYtj3VjO+u9Iuv7uM4M37W3DJmnGzGCS2xb2n9WqRinCqEBwM7uTMM+AjwQktsuyWY1RyW7mrgoBrLzwLucmcr4K70GOBjwFbp35eAy9LyLwOnAR8Hvp6WfQX4nTvz6hzPBsDu7txrxrrAL4BD3BkO7AiMXpHXtzqlysM6wG+hfkVG2j4zDLgJGO3OYHd2Bv4T6L8Sm+tFne+TGUOBfwd2BYYDB5uxZVr938C57owAvpMeA5wC7AL8HDg6Lfs+8K0Gx/BxYII7b67O1+bOK8AsMz64EtuSdqa1Xk/d2dudEencux+4MS2/MCz/T+Af7rwGHAVcTnEenw5gxiHAOHdm1tpHulafAFyTFv0a+FLa9lDg+tX2Ahu7iuI3RtqBlmoxHgjMcWcBgDtzmr74Zkw341wzxpoxyYxt0vJuqdX2odRaemhaPij9ZTo2/dsz35kZu6TnDG6wnePMGGXG3RQV3Ap37gVeq/FaDqU4GUn/fyos/4077s4DQC8zBgKLgK7p3yIzegGHAL9p8H4dDmUL0voU40m/mo5rgTtPpNdwtRWt1P8y4xkzjgjvwZlmPJxaFM4Ny29OrV1TzPhSjfeurxn3m/GJettJn8ETZvwGmAxsAoyi+KGT9mt/YJE7lzctcGeCO2PSH1AXptahSWYcCWBGdzPuCuf/oempFwCDU0vQhdl+tgUedGeeO4uBfwCfbtolFC28QE8oL7DvAZ1Yeh7uDbzkzlMNXs/ngD+/T6/t5rQ/keVpdD39Tvq9nmzGL9IfdE2tvD9K18En0/cfM7pY0Qsy1YybgC5NOzHjMit6XabEa8jyWNHD8iGK73TuKOAPKW66NnYClqRK7+ks/WO2lg8BY9N5D7AhMCu9D0vceSwdw67pOjYuXR+3TsuPM+NGM/5qRa9vuS8zjk/vzUOw9I9UMw6xolV6nBU9o8v8MZwauaZb6qGSNs5TTW9V/oF3Bx8P/iT4peD7hnXTwU9J8UngV6b4fPDPp7hXem438K7gndPyrcAfSfF+4LeC7wn+KPimy9nOceAzwPs0OO5B4JOzZXNDbE2P0773CuvuAh8Jvin4aPD7wYeB/xh8v+W8X78GPyQ8vhL8ZfA/gH8OfJ20/GrwG8DXAd8OfFpafiD4L9LxrZOObZ+0rk/6vwv4ZPAN0uO3wfuDPwh+QKPtpPflPfDds+N+qml7+tf+/oGfCv7TOusOB78DvEP6nj0PPhC8I3iPVKYv+LT0fVvm3Avb2jadxxuk34P7wS8J654HfwH8RfDN0vID0u/CLeA9wf/e6NxPz3kOfP3347WBbwQ+aU1/hvrX+v/R+HraJ8S/bbqOpGvQj1P8cfA7U/xV8KtSPAx8MfjIuK30vR4NPixsa2SD4zsG/I81lncFfy1styf4beCPgH84nWPHLee1n0uqL6TH3wF/Hfwm8C+ztG7QA7xjij8C/qcUHwf+TNp353SOb5LO1+fB+4GvB34f+M/Sc3qDp6Fr/YvhfTyuqUx6fDb419b090P/Vv+/FmkxdudtYGeKFINXgOvMOC4UuTH9/ygwKMUHAmeZMZ4idaAzsCmwLnCFGZOAG4Dtwna2ZWnawfPL2Q7AHe41W4Wb+7ocaDjQszvPu7OfO3sA84CNgalm/NaM66x2juJAivepaRtfBD4MPESRjnFVKHuzO+958Zdy01+yB6Z/44CxwDYUKR4Ap5oxAXiAoqW3afm6FC3n33DnjmZs5zkvWsajl6F+bpi0a3sBf/CiVWc2RSvvLoAB55sxEbgT2IjlpCe4MxX4EfB3ip6V8cCStPorwBnubAKcAfwyPecOd3Z25xCK3p3bgSFm/NGMK6x2vn8fd956n16bzh1pluVcT/dPrZuTKFpXtw9PrXWd3Qf4XdruRGBiKP9ZM8ZS/P5vT/Va20hsFY4OAe5ruua684Y7n3BnJMX15RAoz8c/WronJ5NfG88DRlL8FhzN0p7WnsANVtwj9FOq78Ndad/vAo8BmwG7UaRJveLOQqjce7Ax8Lf0np6ZbSvSOdxOtNiU0O4soaiYjk5fsGMp8niBokuI4uLWtE8DDveUNtDEiuT62RS5hesA74bVsygqvjuytAu13nZ2A95ZiZcy24yB7sxKqRIvp+UvUlQ0m2yclkU/oMhpPBW4EpgOnM+yXajz0+souTMJmGTGb4FnofwhXBCKWfj/h+78PG7DjP0o8tH2cGeeWfmHAsBiih/Mj1Jc2BttZxC137vO6dilfZoCS9N5mulzQD9gZ3cWmTGd7Ltfizu/JFV6zTgfmJFWHUuR0w/FH85XxuelCvBxFN/zWylSMI5Ix3FFtpvFZqzjznvvw2vTuSPNVut6asa1wKXASHdeSNfK+H2rdZ2tyYzNKRphdnHndTOuphnnpRl9KXKGD6ux+v9Ru8IM8G2K6+NRwD+BP1JU5D+alat1bXwauMyMK4BXrLhH53vAPe4clq5Xo8NT4jVzue8FcAnwE3dGpWvod+uU0zncTrTUzXdbm5UtjQAjWP6Uh38DTgk5Ujum5T2BWeli9QWgQ3jOXOATwA/TF7jRdlbWKIqLL+n/P4flx6R8w92BN9yL3Ke0332BmV7kNHalyHl8L8W5qVDcTJTyFPcL65r73p1gRve0jY3M2JDivXs9VYq3AXYPz3GKmxq2MeOby9nOMtL7O4Cisi/t091AJwu562YMS/mMY4AjzehgRj+KlqqHKL6TL6eK4/4UrTcAb1Hk19fU9D00Y1OKym3TzTgzgX1T/CFYJof4TOBidxZR5FM69c/DJ4At3qfXNgSqI+CI1NLgetpUYZyTfrOb84fcvaSbUa24qXVYWt6DovHjjZRT+7FmHt4RwK2pNTYec0+K8/LP+RPSa9nYndEsvTY6Id85KK+N6bmfaLq2U/RmLqGoB/RkacPUcc047geBfc3YwIob3j8T1sVtHbvMM5fSOdxOtFSLcXfgEituPFsMTINlb/zKfA/4X2CiFaMePEtxJ+6lwJ+sGK7lr2Qtl+7MNuNg4C9WDJ9SbzsNmfEHYD+grxkzgHNSK9UFwPVm/BvFj9Fn01Nup7iLfRpFysTxYVtG0VJ8ZFr0C+D3FO/vV2rs/jaK0SyupGi1/YYZP6f4a/QdlnOiu/N3M7YF7rfiJ+Nt4PMU79eJZkyluOg/kD1viRlHAaPMeMudS+tsZwnL2hl4wJfeFCHtjDtuxmHA/6Y/rt6l+EPpdIpWoD2ACRQXvW+485IZvwduSa1ejwCPp229asZ9qSv0L+6cme3uT6llaBHwH+7MTcv/HbjIiht53oVKRfYDwK7u5Y1ElwAPU1xIy+GigtsofgOmvQ+vbf+0P5HlqXk9dWduajWdDLxE8d1ensuAX6VrwlSKXkPcmWDGOIrv7AvAfc08tv9HcY3MHQb83b1mT+MPgLNT/AeKm/bOohhVJvcXihGQmnwB+KkZ8yjei8+l69h/A78241s047xKPcDfpRhNYy5FelaT71KkZbxO8Qfy5nU280HqtyZLG5ISzuX9ZsY/gYPDBb9VM+MiYJT7siN8iKyNUqrUb9w54H3Y173Aoe68vrr3JbI2s2L0jG944xFl3lepJ/qr7nxhTR+LrH6a+W7N+RpLbxJcG0xWpVjakpQKdYVZOfzbapHSL36iSrFIs5xFcRNea9KXIk9a2gG1GIuIiIiIoBZjERERERFAFWMREREREUAVYxERERERoAUn+FhZffv29UGDBq3pw2hXpk+fzpw5c2z5JZunTXyGEybA4joj0XXsCMOHv7/HswIeffTROe7er6W21xo/zwULlo7Z36lTp1Xe3vz5S8fp79Kl1nCq77/28Dm2J+3t85wzZ07l8eI6v6frrFNtj1tvvfXKuFevXi1/YKuoPX2OjS6D0OovhcvV3M9yjVeMBw0axCOPPLKmD6NdGTlyZItur018htbg74TFi6EVvz4zW96EMCuktXyeS5YsHU57+vTpZTx48OAVfj5Ahw5L5wqaNGlSGQ8dOrRSzhp9F1ajtvQ5DhgAs2fXXte/P7z00vt7PGtCW/o8m+OKK6oTS86du3Qk0lhJ7t69e6XcxhtvXMaHHVZrQr01qz19jsv76Wvll8Llau5nqVQKERFpUfUqxctbJyKypq3xFmMRkVoWLVpUxi+88EIZN2oxjsNPxhbi3MyZM8t4hx12WNlDFGnV8uFY6/WG5OViC++6665bWRd7Yjp2XFqFyFOc6u0rXx7Tmg466KAy/stf/lLz+fnxxWMQaQlqMRYRERERQRVjERERERFAFWMREREREUA5xiLSSnXu3LmMr7zyyjLOh3QaMWJEGTcaUeLPf/5zGV900UVl/NGPfnSVjlOktWqUY/zee++VcT6EWp5XHJ188sllHPOKBw4cWCkXh2F79913y3jhwoWVcuuvv34Zjx8/vu5+o5hX3Gj0GZGVoRZjERERERFUMRYRERERAZRKISKtVByubcyYMWX88MMPV8oNGzasjI8//vgyPu+88yrlYnduPqmHSFuUp0jEc6pRusTtt99exv/zP/9TWff000+XcZ8+fco4T2PaaKONyjgOj5inPsTnxdSPPDXjzDPPLOPTTz+9jJU6IS1NLcYiIiIiIqhiLCIiIiICKJVCRFqp2NU7YMCAMo6zXgE8/vjjZfwf//EfZRxHtQDo3bt3Gffr16/FjlOktYojT0D99Imjjjqq8vj6668v4+7du1fWde3atYxjGsTbb79dKTdr1qya+4oz3QF06dKljGOaxYIFCyrlzj777DK+8MILy/iSSy6plDviiCPKOP+t0Cx50hxqMRYRERERQRVjERERERFAFWMREREREUA5xiKyFoh5iC+++GJlXZw5K86KF2flgupwbd26dWvpQxRZq9xzzz1lfPPNN1fWbbbZZmUch3iDZfN2m+Qz2k2fPr2Mt9tuuzLOc4fnzp1bxvG+gPwegXjOxmM64YQTKuXiTJhbbrllZV0cDq7RLJnSvqnFWEREREQEVYxFRERERAClUojIWiB2xcaZt6D+EFT58phKEWflyqm7VdqKfOa76Oc//3kZ57PHxXSJfKa6eH7E4eDy8y0+jjPf5SlO9c63uDw/prjt/DWeccYZZXzLLbdU1ul8luZQi7GIiIiICKrp1HjzAAAZ4klEQVQYi4iIiIgASqUQkVYi7zqN3Z7xjvR89qp6XbH9+/evlHv11Vfr7kukPYjf+3/+859lHGezg+qoD3n6QdxGLJenSMT0jJhy8c4771TKxRFn4rYbnaMxraJHjx6Vdffee28ZT5o0qbJuhx12qLtNkSZqMRYRERERQRVjERERERFAFWMREREREUA5xiLSSjQaSmnatGll3GgIqjir1ltvvVVZt8EGG5Txc889t1LHIbI2u+6668r4tddeK+M8TzfmBOfnQ8+ePct43rx5ZZzPkBeHeYv3CMRtQ/WcjbPdNcptbrQ8Pv7xj39cWXf11VfX3IZIpBZjERERERFUMRYRERERAZRKUdOll15axpMnT667rhHNniXScu65554y3nTTTSvr4ixYeTdtFM/Dxx9/vAWPTmTt8K9//auM43BqeRpEtN5661Uez58/v+bz8pnv4pBqvXr1qrv9eK2M6Rd5ylS9a2rcD1Rf15gxY+ruV6QetRiLiIiIiKCKsYiIiIgIoIqxiIiIiAjQynKMY+4SVKeKbFQuz4GqJ+Ye5W699dYynjlzZhlvuOGGlXLHHHNMGf/gBz8o40022aRSrl5eccyhWpHjE2lvnnrqqTLu169fGedTz0ZxKKn8HIyPZ82a1RKHKLJWGTt2bBnHHN58yLN4Tc3Po3fffbeM4/Bqea5vvfMt31696/fChQvrlov7yo89/j7kU12LNIdajEVEREREUMVYRERERARoZakUMU0B4OSTTy7jfffdt4zrpVisijgM26677lrGeTfPxhtvXMZxFqE85eKwww4r4/XXX7+M83SJmFpRb2af5dFwcNIWxW7f2K2af9/rDRkVu3yh2nU8Y8aMFjtOkbXF008/XcbxWpRfe+Kwh/mwaR07Lq02NEppiOXiNvKh4fIUjHr7rVcuT0+M+3377bdrPkekEbUYi4iIiIigirGIiIiICNAKUinee+893nnnHWDZ7s1Ro0aV8bx588p46NChlXJ9+vQp43gXaj4L1vPPP1/Gv/rVryrrBgwYUMZ9+/Yt41tuuaVS7tBDDy3juXPnlvHtt99eKRdn1tpiiy3K+IADDqiU22yzzVhReddRvW4vjXIha7MHH3ywjOP3Ov/+xy7cRnfaxxSMgQMHlvG0adMq5bbccsuVPGKR1m327NllHK9zzU1vgOo5Fs+pPMUpbiNeo/JycXuxXD6TXjzG5qYPTp8+vfL4zTffLOMePXo0axvS/qjFWEREREQEVYxFRERERABVjEVEREREgFaQYzx//nwmT55cc11T7jHA73//+zIeNmxYpVwcUi3Gee7gpEmTyjifVWfvvfcu4zhM1Ec/+tFKuZjDHPd10EEHVcq9/PLLZfzkk0+W8f33318pt+2225bx9ttvX8YjR46slIszf+W5w8ollrZoypQpZRzzEPMhFOOQTDH3sNFMXDFf8dVXX62UU46xtFUx7z5eN/LrYTxX8pz+RnnFUcwXjvnM8X6h/HE8pvweoSgeU6NyuSeeeKKMd9lll2Y/T9oXtRiLiIiIiKCKsYiIiIgI0ApSKZYsWVIOe/baa69V1sUZbN54440yvummmyrlevfuXcaxizTOOAewxx57lPGQIUMq62JXbRwObs6cOZVysdsnDhOXH3tMudh0001rxlAdPmbMmDFl/PDDD9fdXq9evSrr4pBvcQa+bbbZplKuU6dOiKwt4lBLMX0iT5GIj+NvRt49XO85Tz31VGXdbrvttsLHKtIavfjii3XXxTSIlZ11tZG4zZjukJ+/8Zqdz3ZXT3xOnlrV6LU8++yzZaxUCqlHLcYiIiIiIqhiLCIiIiICtIJUinXWWYdu3boB1dEbAI4//vgyHjRoUBnnaQvvvvtuGcc0g86dO9ctN3HixLrH1L179zKOKQxQ7Z596aWXyjjvzomz6sTnxNQJqN6tG1MzcvHY44gXADNnzqx5vN///vcr5T7/+c8D1Rn7RFqrOFPl1ltvXcb5LF1R7B6OaRVQf4bIOFqNSFsSR2FoJB/ZobkpDY3EESbiyC/5KErxOh2Po9ExxXSM/NrbaJSKWbNmLe+wRdRiLCIiIiICqhiLiIiIiACqGIuIiIiIAK0gx3ju3LmMGjUKgIEDB1bWxdzZmJu7xRZbVMrFIdBi7lF8PsCCBQvKOJ/NJz+mJnGYOIB11123jOPQaI1yjKM8Z7l///41jykfairmYeW50/G9ia85n5XoJz/5CQCzZ8+ueWwia1J+Tsa8+phf2GgYtphfmH//4/kf8xzjvQIibckzzzzTrHJ5Pn4c8iw/j+K52KhcFIcKzc/zeM42d78xzss1yjF+5ZVX6q4TaaIWYxERERERVDEWEREREQFaQSrFggULmDZtGgCDBw+urIsz0E2ePLmMZ8yYUSlXb7iyRl0q+brYPRvjvJsmdgnFbpl8VrkuXbqUcUy/yMWZ9eIxvfXWW5VyMb0jXxeHl4vdz/mMXk3baNQVLbKmPPfcc3XXxfP6nXfeqayL51e9btn8cUxHisPCibQl+dCe9eTXuZjukA+v1lz1Zr7Lz8u47xjnKRfxGhtTKfLhGxtdb/OhXkVqUYuxiIiIiAiqGIuIiIiIAK0glWKdddYpu0kfeOCByrrY5R+7PvNUgHnz5pVxHA0izioH8Pbbb5dxo1EpYtdRfrdufBy7c/JRKaLYtRPTHqDa1RVfRz5DXkyRyLuO4jHFUTnicwDOPfdcAM4555y6xyqypjz++ON11zXqOo3nXiyXn+OxCzeeMy+++OKKH6zIWuDpp5+uuy6eK3m6xPz588u4UWpCIzF94gMf+EAZx1nwoHqdajRzZbzu9+7du+724vHm29CoFNIcajEWEREREUEVYxERERERQBVjERERERGgFeQYb7rpplxyySVlHPXp06eM47BmeY5xzDGMebr50Czrr79+GcdcXKjmW8W8pHxYt5h7FfOh8hzjeIz1tt1oXXztAL169SrjfOa7WHbrrbcu4wMOOIBaLr744prLRdak5ub65udu1GhYqJibHM/dfPhDkbYiXjeheo2J50N+nYvl8vMoiuvycvHaNmvWrLr7qvec/FoZZ6Hdf//9y/i2226rlIu/D3l+dJ6PLFKLWoxFRERERFDFWEREREQEaAWpFB06dCiHXjn//PPX8NGIyJqSpzQ0t9s3dp3GdflsXlHssm2UmiGyNsvPqZhaENMON9tss0q5mHb44IMPVtZttNFGZbxgwYIybnS+NVoXxfM3nqNQHW41ikO3QTVdIk/HaDRMq0gTtRiLiIiIiKCKsYiIiIgI0ApSKUREYNlRKWK3b+xizbtD63XT5l2x8XHcXuwOhmraxsrO+iXSGuSpFF26dCnjOGrTiBEjKuViCkI+I20cfaJRikQs1yhdKW6jXpxvL6ZPDBkypFLuzjvvLON89ttGI2KINFGLsYiIiIgIqhiLiIiIiACqGIuIiIiIAMoxFpFW4s0336w87tSpUxk3mn2rQ4cONcvlOYoxxzjPP45i7mX//v0bHLFI65bn49fLmY8zyQFMmTKl7jYbnTtRPP/i8G9xmDhYueESN9hggzLO84hjjnF+rI1+R0SaqMVYRERERARVjEVEREREAKVSiEgrkc9s1dyh0mL3aIxjikWj7cXh2QDmzp1bxkqlkLVZTEeC+jO/HXrooZXH48ePr7vNerNQ5mkLcV08LxcuXFgpF58Xy+XDKEbrrbdeGe+zzz6VdT/84Q/LOE+n6tGjR91tijRRi7GIiIiICKoYi4iIiIgAqhiLiIiIiADKMRaRVuLdd9+tPO7WrVsZx9zIPE8y5jLGoZ/y/MqYcxzzHDfffPOGxyGytoq5uLnu3buXcT7k2TvvvFPGeZ5uPN+aO8VynJo6z0WO53OjKaGjmCucn+fxNyA/vno51iKRWoxFRERERFDFWEREREQEUCqFiLQS9913X+VxnC0r6tKlS93Hses4H54tds3GYaHy1IknnniijIcPH768wxZptWI6ElSHRGyUMhTPnTxVod4MkvnwiPF8iykNeYpEfBy317FjtXrSuXPnMo6zZOYzZkb5sccZ80TqUYuxiIiIiAiqGIuIiIiIAEqlEJFW4sQTT6w8jjNYxVEk4h3uALNmzSrjPn36lHE+o11Ms4hpGvPmzauU692794octkirdfvtt1cez5kzp4znz59f93nTpk1r1vYbjRYT05ViWkSeShFTMOKIEvH5uYkTJ5bxt7/97br7FVkZajEWEREREUEVYxERERERQBVjERERERFAOcYi0kqcd955lcc77LBDGT/22GNlnOdGDhkypIxHjBhRxnnucNeuXcs4Dsl21FFHreQRi6xd8hnu6on5+HGYNKgO5RbjPKc/5vrGbTTKRY7ycvG+gG222abusYusKrUYi4iIiIigirGIiIiICAC2poc2MbNXgOfW6EG0P5u5e7+W2pg+wzVOn2fboM+xbdHn2Tboc2w7mvVZrvGKsYiIiIhIa6BUChERERERVDEWEREREQFUMRYRERERAVZXxdjsbMymYDYRs/GY7dZC2x2N2ciVKmN2MmbTMHPM+oblhtnFad1EzHYK647F7Kn079i0rBNmf8VsMmYnhbK/qDx32f1/CrPvpHjrdJzjMZuK2S+a9wasgOa8V42f/z+YfagFj0jWMmYsMWO8GVPMmGDG18zenz+mzehtxk1mTDTjITOGhnWnmTE5HdfpYfmPUvnfhGWfj2Vq7GegGbemeD8z3jBjnBlPmHGvGQevrteY9nmtGVutzn1I22TGgPT9edqMR8243Ywhy3/mMtvpZcZJDdZfZcbLZkzOlvcx4w4znkr/907LzYyLzZiWzsed0vKt03FONGOPtKyjGXea0XXZPZf7+V8z9knxumZckPY51oz7zfjYir7mtK3T437TcfRemW1J29LyFzmzPYCDgZ1wHwZ8BHihxfez4u6jOJb8btCPAVulf18CLgPArA9wDrAbsCtwDma9gY8C/wSGAV9IZYcDHXAf22D/3wAuTfHFwE9xH4H7tsAlq/jaWpZZB4pjOmtNH4qsUfPdGeHO9sABFOfKOXkhs9UyUdB/AePdGQYcA1yU9jUU+HeKc3I4cLAZW5rRE9gplV9oxg5mdAGOB/6vwX6+ClwRHo9xZ0d3tgZOBX5mxofzJ7Xga76M4rdBpNnMMOAmYLQ7g93ZGfhPoP9KbK4X1K8YA1cDB9VYfhZwlztbAXex9HpR+5oKXwZOAz4OfD0t+wrwO3eqs/EkZmwA7O7OvWnR94CBwFB3dgI+Baxf67nNcDpUKuS/pfH7IO3E6mj9GQjMwX0BAO5zcJ8JgNl3MHs4tbb+AjNLy0dj9iPMHsLsScz2Tsu7YHZtalW9CehS7sXsMsweSS3T5y73qNzH4T69xppDgd/g7rg/APTCbCBFBfgO3F/D/XXgDoofh0UUJ9O6gKVtfA/4dt19mw0BFuA+J7xHM8KxTUrljsPsxtQi/RRm/x22cSBm92M2FrMbMOueltd+T5c+bx3Mrsbs+8vZzvT0GYwFPoP7c8AGmA1o8K5KO+HOyxQXuZNTi9BxZowy426KiyJmnGnGw6lF6Ny0rJsZt6UW58lmHJmWX2DGY6ns/9TY5XbA3WnfjwODzOgPbAs86M48dxYD/wA+DbwHrJsqDF0pztOvA5e4s6jG9pscDvy1zmseD5wHnJyO+WozLjfjQeC/02u7KrVojzPj0FRu+7RsfHp9W9V7H4AxwEdW0x8X0nbtDyxy5/KmBe5McGdMOj8vTN+zSeGc627GXamldVLT9xW4ABicvq8X5jtKldLXahzDocCvU/xrikpq0/LfuOPuPAD0MmMgS6+dXYFFZvQCDoGlPTw1lOdnat39d+AUdxakY5vtzvVp/VHpdU0240dNGzDjMjMeST1MTb9LpwIfAO4x455UdBSgaTBltVSM/w5skiq4l2K2b1j3M9x3wX0oRSU3dlN2xH1Xir/imlqlvgLMS62q5wA7h/Jn4z6SouV2X8yGreTxbkS1RXtGWlZv+R3AIOAB4GLMPgmMLSv/tX0QiK3JPwXuxuwvmJ2BWa+wbgRwJLADcCRmm1CkfnwL+AjuOwGPULR0wfLeU/g98BTu31rOdgBexX0n3K9Nj8emYxfBnWeADsCGadFOwBHu7GvGgRQtRLtSfId3Tt2fBwEz3RnuzlDgr6kV6DBg+9TC+/0au5tAUeHFjF2BzYCNgcnA3mZskC6UHwc2cect4HZgHDALeAPYzZ2b670eMzYHXm+6yNYxFojzz24M7OnOV4Gzgbvd2ZWionKhGd2AE4GL3BkBjKT47VjmfUjv6XvANIrWb5HmGgo8WmfdpynOweEUvaQXporpu8BhqaV1f+DH6Q/Js4CnU+/QmStwDP3dmZXil1jaWl3v2vl/FD1BvwbOp2hMOj+dA/V8MLzOLYHn3XkzL2TGB4AfAR9Kr30Xs7KifrY7ZV3BjGHuXAzMBPZ3Z38Ad14HOqXfJ2nHWr5i7P42RQX2S8ArwHWYHZfW7o/Zg5hNovgCbx+eeWP6/1GKiifAPsDv0nYnAhND+c+m1s1xaTvbtfRLqcl9Me5H474jcANFRf7HmP0Esz+minJuIMV70bSNX1G0fN0A7Ac8gFmntPYu3N/A/V3gMYoKwe4Ur+8+zMYDx6bl0Pg9/TkwGfcfpMeNtgNwXXbcL1P8VS1Syx3uZUvSgenfOJZWJrcCJgEHWJH/u7c7b1BUWt8FfmnGp6FmN+oFFC1N44FT0naXuDOV4gL4d4rK5XhgCYA7/50u7l+j6MX5jhlfNON6M75VYx/V87I2yx7f4F7sL73es9IxjgY6A5sC9wP/ZcY3gc3cmV/nfWii80xa0l7AH9xZ4s5sil6VXSi+y+ebMRG4k6KyujKpF8twx4GGkyK487w7+7mzB8U5vzEw1YzfmnGd1c6Pbs45CsXrG+3OK6kn6fcU9QeAz5rR3LqCzkVZTTfSuC/BfTTu51B0Qx6OWWeKHNsjcN+BIq+vc3hWU6vNElhOt6LZ5hTdpB9Oecy3ZdtaES8Cm4THG6dl9ZZHJ1F0A+1OcbE/EvhajX3MX+b43GfifhXuhwKLoby5KLZeNb0XRpHWMSL92w73f2vGe/oviopz07La21nqney4O6djF8GMLSi+ky+nRfH7YsAPU8V0hDtbuvNLd56kaFmeBHzfjO+kC9euwB8pejiWSWVw5013jk+trscA/YBn0rpfurOzO/sArwNPZse5YzqeJ4DPuPNZiq7i/Ca3Zc/LZe0ITA2P89d8eHjNm7oz1Z1rgE+m7d9uxodqvQ9hOzrPZEVNodqD2hyfoziPdk7n1WxW/roJMDu1RJP+b/pdaM618wcUvZenAldS5Nkvc/8C1XN0GrCpGT2ae4CpV+jrwIdT79Ty6go6F2W13Hy3NWbxAjSC4oa3pi/jnJTXekQztnYvcHTa7lCKrhCAHhQXqDcw6w8rd1dqMgo4hmJ0iqKC6z4L+BtwIGa90013B6ZlpOPpTXFR/w1FztR7FH8xd2FZUym6gZqeexBm66Z4ALABy/5wRA8AH8Rsy/ScbilveXnv6S8pupevx6xjg+3UMwSqdyJL+2RGP+By4GepdSj3N+AEM7qn8huZsWHq4pznzu+AC4GdUpme7twOnEGNNAIr7pRfLz38InBvUxeqWZHKYcamFN3G12RPb8r5X5ci9QOK8zO/8/1JlvZO1XrNw9J26t289zfglNQd3VQhb/oD4pnUXftnYFit9yFsR+eZrKi7Kbr9v9S0wIxhZuxNkbd+pBkd0nm7D/AQ0JP/3979vNgUxnEcf39CUcqG8gfYWNuwkGwsZDMLNn5sSEnJj8hiokxsRCYL8iNJmlISQqRERJnCEEKxEBPR+D018rX4Psxx5/iRugbzeW1u996Zc8493ec83/M83+9z4VkEfRLT6Z8tfMPvFbAdI2cdKY9HK68vKLnOk4FXlZQLJKaRaUX36e8769onVPrOUqC3F2j/cm2QGCcxu3y+aRJjJYaRucLnqcQKpUahGit887lLOx4PPPqNc2H/kWYUfIwGtpe82Y/kXd5iInqQdpMdQDdw9Re2tQPYh3SHbCCZaxRxA+kacJfMZbr00y1Jy8i70vFAF9JJIhaRgePMcpzvySp2iHiJ1FY5zg1EVAsQ1gEbifiEdBpYSo4G7WSgC2S6hcjf4J4BtCP1lvdXE9GNGmdti4jnJR2lo5Jy0UrEvZ+e04itSGPIitu5wMDtNIy4lfM1grwgddYflA0Bo0qawAiyLR8Attb9YQRnJCYCl8vX+C0wj/wObZb4RBbfLCE7o6MSI8lR15U1m5wI7JcIcnSsOrNxuOQB9gFLI+j58kbJK+yM4El5fl3iJtAVwY2GY36nXOpqQgQPystTJa6RnfQzYFlEFhfWaAO2AV3KZewekjfLc4D5En1ku9xETvU2ngdKZ/0hgu7v7MNsgAhCogXYVlJ2esmAbjm5atIUMk8/gDURdEscBI6X9tBJ9p9E8ELiknI5tlONecYSHWTK31iJx8D6CPaS6U6HJBaSg19zyr/U96l8DT5b4Wvx6S4y7WE4pU00OEGuZrGnPG8laxJuS/SSQe+6CJ5KrAXOkdeUExEZqJf2XBcr7CJrHp6UPONJwJUyo2VDmDJOs6aT2oHjRJwd7EP5JVILueTe91fbMPvHleBiUkRtDvKf2P8K4HUJNMysgcRFYFb1BrhJ+2kHjv3gRtiGCP/y3Z+zifqpor/VcGDLYB+EWTNFcITBnTrtoX/JKzMbaBVZ1NpstxwUG3jE2MzMzMwM8IixmZmZmRngwNjMzMzMDHBgbGZmZmYGODA2MzMzMwMcGJuZmZmZAfAZbxFcdiCuvCYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted label, and the true label\n",
    "# Color correct predictions in blue, incorrect predictions in red\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions, test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions, test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset\n",
    "img = test_images[0]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.0000000e+00 0.0000000e+00 3.7819336e-19 0.0000000e+00 6.0636381e-30\n",
      "  1.9132604e-03 0.0000000e+00 2.7599052e-01 4.6169405e-12 7.2209626e-01]]\n"
     ]
    }
   ],
   "source": [
    "predictions_single = model.predict(img)\n",
    "\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(0, predictions_single, test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
